diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs
new file mode 100644
index 0000000..b60c33f
--- /dev/null
+++ b/.git-blame-ignore-revs
@@ -0,0 +1,2 @@
+# ruff format, line length 100
+65ca0f267e1804971ddb71ea2f85a02cad9f8294
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 5961063..85d169c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -6,10 +6,14 @@ on:
 jobs:
   label-gate:
     uses: AustralianCancerDataNetwork/cava-devops/.github/workflows/label-gate.yml@main
+  ruff:
+    uses: AustralianCancerDataNetwork/cava-devops/.github/workflows/ruff.yml@ruff-ty-workflows
+  ty:
+    uses: AustralianCancerDataNetwork/cava-devops/.github/workflows/ty.yml@ruff-ty-workflows
   build-test-sqlite:
-    uses: AustralianCancerDataNetwork/cava-devops/.github/workflows/build-test.yml@main
+    uses: AustralianCancerDataNetwork/cava-devops/.github/workflows/build-test-sqlite.yml@ruff-ty-workflows
   build-test-postgres:
-    uses: AustralianCancerDataNetwork/cava-devops/.github/workflows/build-test-postgres-v2.yml@main
+    uses: AustralianCancerDataNetwork/cava-devops/.github/workflows/build-test-postgres-v3.yml@ruff-ty-workflows
     with:
       postgres-db: omop_graph_test
       setup-commands: |
diff --git a/README.md b/README.md
index d191d25..57559fd 100644
--- a/README.md
+++ b/README.md
@@ -80,8 +80,8 @@ This classification drives traversal filtering and scoring.
 from omop_graph.graph.paths import find_shortest_paths
 from omop_graph.extensions.omop_alchemy import PredicateKind
 
-ingredient = kg.concept_id_by_code("RxNorm", "6809")    # Metformin
-drug = kg.concept_id_by_code("RxNorm", "860975")         # Metformin 500 MG Oral Tablet
+ingredient = kg.concept_id_by_code("RxNorm", "6809")  # Metformin
+drug = kg.concept_id_by_code("RxNorm", "860975")  # Metformin 500 MG Oral Tablet
 
 paths, trace = find_shortest_paths(
     kg,
diff --git a/docs/graph/kg.md b/docs/graph/kg.md
index dfd0d82..f6f5cb3 100644
--- a/docs/graph/kg.md
+++ b/docs/graph/kg.md
@@ -66,8 +66,10 @@ from omop_graph.graph.kg import KnowledgeGraph, KnowledgeGraphEmbeddingConfigura
 from omop_emb.config import MetricType
 
 resolver = Resolver.from_active_config()
-resolved_vector_store = resolver.resolve_vector_store("vector_store")   # a [vector_stores.*] entry name
-resolved_model = resolver.resolve_model("embedding-model")              # a [models.*] entry name
+resolved_vector_store = resolver.resolve_vector_store(
+    "vector_store"
+)  # a [vector_stores.*] entry name
+resolved_model = resolver.resolve_model("embedding-model")  # a [models.*] entry name
 backend = open_vector_store_writer(resolved_vector_store)
 
 emb_config = KnowledgeGraphEmbeddingConfiguration(
diff --git a/docs/mkdocs_macros.py b/docs/mkdocs_macros.py
index 5b09a58..6ff0eaa 100644
--- a/docs/mkdocs_macros.py
+++ b/docs/mkdocs_macros.py
@@ -1,28 +1,27 @@
-import pandas as pd
 import pathlib as pl
-from typing import Optional, List, Mapping
+from collections.abc import Mapping
+
+import pandas as pd
+
 
 def define_env(env):
     @env.macro
     def to_grouped_table(
-        file_path: str, 
-        group_cols: List[int] = [0], 
-        displayed_cols: Optional[List[int]] = None, 
-        sort_cols: Optional[List[int]] = None, 
-        col_mappings: Optional[Mapping[str, str]] = None
+        file_path: str,
+        group_cols: list[int] | None = None,
+        displayed_cols: list[int] | None = None,
+        sort_cols: list[int] | None = None,
+        col_mappings: Mapping[str, str] | None = None,
     ):
         if group_cols is None:
             group_cols = [0]
 
         absolute_path = (pl.Path(env.project_dir) / file_path).resolve()
-        
+
         if not absolute_path.exists():
             return f"**Error:** Could not find file at `{absolute_path}`"
-        
-        suffix_loader_map = {
-            ".csv": pd.read_csv,
-            ".xlsx": pd.read_excel
-        }
+
+        suffix_loader_map = {".csv": pd.read_csv, ".xlsx": pd.read_excel}
         loader_fn = suffix_loader_map[absolute_path.suffix]
 
         df = loader_fn(absolute_path, usecols=displayed_cols)
@@ -35,36 +34,41 @@ def to_grouped_table(
             df = df.rename(columns=col_mappings)
 
         df = df.dropna(subset=df.columns[group_cols])
-        
+
         span_counts_map = {}
         for i, col_idx in enumerate(group_cols):
-            relevant_cols = group_cols[:i+1]
-            group_changes = (df.iloc[:, relevant_cols] != df.iloc[:, relevant_cols].shift()).any(axis=1)
+            relevant_cols = group_cols[: i + 1]
+            group_changes = (df.iloc[:, relevant_cols] != df.iloc[:, relevant_cols].shift()).any(
+                axis=1
+            )
             span_counts_map[col_idx] = group_changes.cumsum().value_counts(sort=False).tolist()
 
         table_style = "width: 100%; border-collapse: collapse; margin-bottom: 1rem;"
         cell_style = "border: 1px solid var(--md-typeset-table-color, #e0e0e0); padding: 12px; text-align: left;"
-        header_style = cell_style + " background-color: var(--md-default-bg-color, #f5f5f5); font-weight: bold;"
-        
+        header_style = (
+            cell_style
+            + " background-color: var(--md-default-bg-color, #f5f5f5); font-weight: bold;"
+        )
+
         html = f'<table style="{table_style}">'
         html += "<thead><tr>"
-        
-        for col in df.columns: 
+
+        for col in df.columns:
             html += f'<th style="{header_style}">{col}</th>'
         html += "</tr></thead>"
-        
+
         html += "<tbody>"
-        
+
         span_idx_map = {c: 0 for c in group_cols}
         rows_to_skip_map = {c: 0 for c in group_cols}
-        
+
         for i, row in df.iterrows():
             highest_change_level = -1
             for level, col_idx in enumerate(group_cols):
                 if rows_to_skip_map[col_idx] == 0:
                     highest_change_level = level
                     break
-            
+
             if highest_change_level == 0 and i > 0:
                 html += '<tr style="border-top: 2px solid var(--md-default-fg-color, #666);">'
             elif highest_change_level > 0 and i > 0:
@@ -77,16 +81,19 @@ def to_grouped_table(
                 if j in group_cols:
                     if rows_to_skip_map[j] == 0:
                         span = span_counts_map[j][span_idx_map[j]]
-                        group_style = cell_style + " vertical-align: middle; font-weight: bold; background-color: var(--md-code-bg-color, #fafafa);"
+                        group_style = (
+                            cell_style
+                            + " vertical-align: middle; font-weight: bold; background-color: var(--md-code-bg-color, #fafafa);"
+                        )
                         html += f'<td rowspan="{span}" style="{group_style}">{display_val}</td>'
                         rows_to_skip_map[j] = span - 1
                         span_idx_map[j] += 1
                     else:
                         rows_to_skip_map[j] -= 1
-                        continue 
+                        continue
                 else:
                     html += f'<td style="{cell_style}">{display_val}</td>'
             html += "</tr>"
-            
+
         html += "</tbody></table>"
-        return html
\ No newline at end of file
+        return html
diff --git a/docs/reasoning/grounding.md b/docs/reasoning/grounding.md
index 591586d..65f851d 100644
--- a/docs/reasoning/grounding.md
+++ b/docs/reasoning/grounding.md
@@ -66,7 +66,7 @@ from omop_alchemy.cdm.query import ConceptFilter
 from omop_graph.extensions.omop_alchemy import PredicateKind
 
 constraints = GroundingConstraints(
-    parent_ids=(441484,),   # 'Clinical Finding' — only accept descendants of this ancestor
+    parent_ids=(441484,),  # 'Clinical Finding' — only accept descendants of this ancestor
     search_constraint=ConceptFilter(
         domains=("Condition",),
         vocabularies=("SNOMED",),
@@ -80,7 +80,7 @@ results = ground_term(
     resolver_pipeline=pipeline,
     kg=kg,
     query="chest pain",
-    query_embedding=None,   # pass a precomputed vector, or None to skip embedding-based scoring
+    query_embedding=None,  # pass a precomputed vector, or None to skip embedding-based scoring
     constraints=constraints,
 )
 ```
diff --git a/docs/reasoning/resolvers.md b/docs/reasoning/resolvers.md
index 285c1eb..23df2d0 100644
--- a/docs/reasoning/resolvers.md
+++ b/docs/reasoning/resolvers.md
@@ -28,9 +28,9 @@ constraints = GroundingConstraints(
     search_constraint=ConceptFilter(
         domains=parsed_domains,
         vocabularies=parsed_vocabs,
-        require_standard=True  # We prioritize Standard Concepts
+        require_standard=True,  # We prioritize Standard Concepts
     ),
-    max_depth=6  # How deep to traverse the hierarchy
+    max_depth=6,  # How deep to traverse the hierarchy
 )
 
 # The resolver pipeline stops once it finds a high-confidence match
diff --git a/notebooks/01_quickstart.ipynb b/notebooks/01_quickstart.ipynb
deleted file mode 100644
index 6e6f6c4..0000000
--- a/notebooks/01_quickstart.ipynb
+++ /dev/null
@@ -1,716 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "edd10d93",
-   "metadata": {},
-   "source": [
-    "\n",
-    "This notebook demonstrates the minimal workflow:\n",
-    "\n",
-    "1. Create a KnowledgeGraph\n",
-    "2. Explore a concept neighbourhood\n",
-    "3. Find and rank paths\n",
-    "4. Explain why a path was selected"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "baff3b36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sqlalchemy.orm import sessionmaker\n",
-    "from dotenv import load_dotenv\n",
-    "\n",
-    "from omop_graph.graph.scoring import rank_paths, explain_path\n",
-    "from omop_graph.graph.paths import find_shortest_paths\n",
-    "from omop_graph.graph.kg import KnowledgeGraph\n",
-    "from omop_graph.graph.edges import PredicateKind\n",
-    "from omop_graph.render import (\n",
-    "    render_trace,\n",
-    "    render_path,\n",
-    "    bind_default_renderers,\n",
-    ")\n",
-    "from orm_loader.helpers import configure_logging\n",
-    "from omop_alchemy import  get_engine_name\n",
-    "import sqlalchemy as sa\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9d271c31",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "configure_logging()\n",
-    "load_dotenv()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "11c7ac1a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2026-01-12 10:14:33,396 | INFO     | sql_loader.omop_alchemy.config | Database engine configured\n"
-     ]
-    }
-   ],
-   "source": [
-    "engine_string = get_engine_name()\n",
-    "engine = sa.create_engine(engine_string, future=True, echo=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7533b331",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Session = sessionmaker(bind=engine)\n",
-    "\n",
-    "session = Session()\n",
-    "kg = KnowledgeGraph(session)\n",
-    "bind_default_renderers(kg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9620337e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ingredient = kg.concept_id_by_code(\"RxNorm\", \"6809\") # Metformin\n",
-    "drug = kg.concept_id_by_code(\"RxNorm\", \"860975\") # Metformin 500 MG Oral Tablet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ffb4566d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ConceptView(id=40163924, RxNorm:860975, name='24 HR metformin hydrochloride 500 MG Extended Release Oral Tablet')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kg.concept_view(drug)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "9c280eb3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ConceptView(id=1503297, RxNorm:6809, name='metformin')"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kg.concept_view(ingredient)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a2c4ed32",
-   "metadata": {},
-   "source": [
-    "## Note on domain scope:\n",
-    "\n",
-    "By default, traversal is restricted to relationships within the same OMOP domain (e.g. Drug -> Drug, Condition -> Condition). This avoids misleading \u201cshortcut\u201d paths through terminology metadata. \n",
-    "\n",
-    "Cross-domain reasoning (e.g. Drug -> Condition) requires explicit biomedical relationships and is intentionally out of scope for the default - it also frequently defaults to structural or metadata relationsips such as SNOMED 'has module' which becomes rapidly non-specific if all terms resolve in 1 or 2 steps to an extreme high level parent.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c29ea71b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "paths, trace = find_shortest_paths(\n",
-    "    kg,\n",
-    "    source=drug,\n",
-    "    target=ingredient,\n",
-    "    predicate_kinds={\n",
-    "        PredicateKind.ONTOLOGICAL,\n",
-    "        PredicateKind.MAPPING,\n",
-    "    },\n",
-    "    max_depth=6,\n",
-    "    traced=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "4d319d70",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ranked = rank_paths(kg, paths)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "9e592d22",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div style=\"font-family: system-ui, sans-serif; line-height:1.4;\">\n",
-       "        \n",
-       "        <div style=\"margin-bottom:16px;\">\n",
-       "            <div style=\"margin-bottom:6px; color:#666;\">\n",
-       "                [depth 0]\n",
-       "            </div>\n",
-       "            \n",
-       "    <div style=\"\n",
-       "        border:1px solid #ddd;\n",
-       "        border-radius:6px;\n",
-       "        padding:6px 8px;\n",
-       "        background:#fafafa;\n",
-       "        display:inline-block;\n",
-       "        max-width:420px;\n",
-       "    \">\n",
-       "        <div style=\"font-weight:600;\">\n",
-       "            24 HR metformin hydrochloride 500 MG Extended Release Oral Tablet \u2705\n",
-       "        </div>\n",
-       "        <div style=\"font-size:0.85em; color:#555;\">\n",
-       "            <code>RxNorm:860975</code>\n",
-       "            \u00b7 Drug\n",
-       "            \u00b7 Quant Clinical Drug\n",
-       "        </div>\n",
-       "    </div>\n",
-       "    \n",
-       "        \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 Standard to Non-standard map (OMOP)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 24 HR metformin hydrochloride 500 MG Extended Release Oral Tablet\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 24 HR metformin hydrochloride 500 MG Extended Release Oral Tablet\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 24 HR metformin hydrochloride 500 MG Extended Release Oral Tablet\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 24 HR metformin hydrochloride 500 MG Extended Release Oral Tablet\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 24 HR metformin hydrochloride 500 MG Extended Release Oral Tablet\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px; color:#888;\">\n",
-       "                    \u2026 920 more\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 RxNorm to SPL (NLM)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 METFORMIN ER 500 MG - metformin er 500 mg tablet\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 METFORMIN HYDROCHLORIDE - metformin hydrochloride tablet, extended release\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 METFORMIN HYDROCHLORIDE - metformin hydrochloride tablet, extended release\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 METFORMIN HYDROCHLORIDE - metformin hydrochloride tablet, extended release\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 METFORMIN HYDROCHLORIDE - metformin hydrochloride tablet, extended release\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px; color:#888;\">\n",
-       "                    \u2026 237 more\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 Concept replaces</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 Metformin 500 MG 24 Hour Extended Release Tablet\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 Has dose form (RxNorm)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 Extended Release Oral Tablet\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 RxNorm to SNOMED equivalent (RxNorm)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 Metformin hydrochloride 500 mg prolonged-release oral tablet\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 Is a (RxNorm)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin Oral Product\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin Pill\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 Non-standard to Standard map (OMOP)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 24 HR metformin hydrochloride 500 MG Extended Release Oral Tablet\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 Has tradename (RxNorm)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 24 HR metformin hydrochloride 500 MG Extended Release Oral Tablet [Glucophage]\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 Quantified form of (RxNorm)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin hydrochloride 500 MG Extended Release Oral Tablet\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 RxNorm to VA Product equivalent (NDF-RT)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 METFORMIN HCL 500MG TAB,SA\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 METFORMIN HCL 500MG 24HR TAB,SA\n",
-       "                </div>\n",
-       "                </div>\n",
-       "        <div style=\"margin-bottom:16px;\">\n",
-       "            <div style=\"margin-bottom:6px; color:#666;\">\n",
-       "                [depth 0]\n",
-       "            </div>\n",
-       "            \n",
-       "    <div style=\"\n",
-       "        border:1px solid #ddd;\n",
-       "        border-radius:6px;\n",
-       "        padding:6px 8px;\n",
-       "        background:#fafafa;\n",
-       "        display:inline-block;\n",
-       "        max-width:420px;\n",
-       "    \">\n",
-       "        <div style=\"font-weight:600;\">\n",
-       "            metformin \u2705\n",
-       "        </div>\n",
-       "        <div style=\"font-size:0.85em; color:#555;\">\n",
-       "            <code>RxNorm:6809</code>\n",
-       "            \u00b7 Drug\n",
-       "            \u00b7 Ingredient\n",
-       "        </div>\n",
-       "    </div>\n",
-       "    \n",
-       "        \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 Brand name of (OMOP)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px; color:#888;\">\n",
-       "                    \u2026 521 more\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 Has ingredient (RxNorm)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px; color:#888;\">\n",
-       "                    \u2026 113 more\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 Non-standard to Standard map (OMOP)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px; color:#888;\">\n",
-       "                    \u2026 91 more\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 Drug Source to RxNorm equivalent (OMOP)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px; color:#888;\">\n",
-       "                    \u2026 10 more\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 ATC to RxNorm/Extension primary lateral (OMOP)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px; color:#888;\">\n",
-       "                    \u2026 16 more\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 NDF-RT to RxNorm equivalent (RxNorm)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 SNOMED to RxNorm equivalent (RxNorm)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 Standard to Non-standard map (OMOP)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 Form of (RxNorm)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                \n",
-       "            <div style=\"margin-left:20px; margin-top:6px;\">\n",
-       "                <span style=\"color:#555;\">\u2514\u2500 ATC to RxNorm/Extension secondary upwards (OMOP)</span>\n",
-       "            </div>\n",
-       "            \n",
-       "                <div style=\"margin-left:40px;\">\n",
-       "                    \u2192 metformin\n",
-       "                </div>\n",
-       "                </div><div style='color:#a00;'>[terminated: shortest_paths_found]</div>\n",
-       "    </div>\n",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from IPython.display import HTML, display\n",
-    "\n",
-    "display(HTML(render_trace(kg, trace)))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "a23ad680",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(ranked)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "9411377f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "24 HR metformin hydrochloride 500 MG Extended Release Oral Tablet <b>--[RxNorm is a]--></b> metformin Oral Product<br/>metformin Oral Product <b>--[RxNorm has ing]--></b> metformin"
-      ],
-      "text/plain": [
-       "GraphPath(steps=(PathStep(subject=40163924, predicate='RxNorm is a', object=36223144), PathStep(subject=36223144, predicate='RxNorm has ing', object=1503297)))"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ranked[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "8ef85864",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PathExplanation(path=GraphPath(steps=(PathStep(subject=40163924, predicate='RxNorm is a', object=36223144), PathStep(subject=36223144, predicate='RxNorm has ing', object=1503297))), profile=PathProfile(hops=2, invalid_concepts=0, non_standard_concepts=0, vocab_switches=0, ontological_edges=2, mapping_edges=0, metadata_edges=0), steps=(PathExplanationStep(step=PathStep(subject=40163924, predicate='RxNorm is a', object=36223144), traversal_depth=0, predicate_kind=<PredicateKind.ONTOLOGICAL: 1>, reason='ontological relationship (preferred structure)'), PathExplanationStep(step=PathStep(subject=36223144, predicate='RxNorm has ing', object=1503297), traversal_depth=None, predicate_kind=<PredicateKind.ONTOLOGICAL: 1>, reason='ontological relationship (preferred structure)')))"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "explain_path(kg, ranked[0], trace)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "c263ab6a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "24 HR metformin hydrochloride 500 MG Extended Release Oral Tablet <b>--[RxNorm is a]--></b> metformin Oral Product<br/>metformin Oral Product <b>--[RxNorm has ing]--></b> metformin"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "24 HR metformin hydrochloride 500 MG Extended Release Oral Tablet <b>--[RxNorm is a]--></b> metformin Pill<br/>metformin Pill <b>--[RxNorm has ing]--></b> metformin"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for p in ranked:\n",
-    "    display(HTML(render_path(kg, p)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f8bb4bf4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kg-core (3.12.10)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
\ No newline at end of file
diff --git a/notebooks/02_reasoner.ipynb b/notebooks/02_reasoner.ipynb
deleted file mode 100644
index 21b5248..0000000
--- a/notebooks/02_reasoner.ipynb
+++ /dev/null
@@ -1,363 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "12d9d4e5",
-   "metadata": {},
-   "source": [
-    "## Constraint-aware semantic resolution over OMOP vocabularies\n",
-    "\n",
-    "* Pydantic classes (via linkml) produce structured LLM queries\n",
-    "* the LLM proposes candidates\n",
-    "* LinkML defines valid target spaces\n",
-    "* the KG enforces ontological correctness\n",
-    "* scoring / ranking resolves ambiguity\n",
-    "\n",
-    "### Reasoners\n",
-    "\n",
-    "1. Hierarchy reasoner: Strict checks for candidate ⊑ allowed_parent\n",
-    "2. Domain reasoner: Broader checks simply confirming if this concept is in the correct OMOP domain?\n",
-    "3. Vocabulary preferences: List of ordered vocab targets \n",
-    "4. Standardness and validity\n",
-    "5. Specificity / depth: Out of concepts matching selected restrictions, which is at the most appropriate level of specificity?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "7a6fd293",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sqlalchemy.orm import sessionmaker\n",
-    "from dotenv import load_dotenv\n",
-    "\n",
-    "from omop_graph.graph.kg import KnowledgeGraph\n",
-    "from omop_graph.render import (\n",
-    "    bind_default_renderers,\n",
-    ")\n",
-    "from orm_loader.helpers import configure_logging\n",
-    "from omop_alchemy import get_engine_name\n",
-    "import sqlalchemy as sa"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ce23b2dc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2026-01-12 10:15:20,755 | INFO     | sql_loader.omop_alchemy.config | Database engine configured\n"
-     ]
-    }
-   ],
-   "source": [
-    "configure_logging()\n",
-    "load_dotenv()\n",
-    "\n",
-    "engine_string = get_engine_name()\n",
-    "engine = sa.create_engine(engine_string, future=True, echo=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "934b60ab",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Session = sessionmaker(bind=engine)\n",
-    "\n",
-    "session = Session()\n",
-    "kg = KnowledgeGraph(session)\n",
-    "bind_default_renderers(kg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a7ebd037",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# synonyms = pd.DataFrame(\n",
-    "#     session.query(\n",
-    "#         Concept_Synonym.concept_synonym_name, Concept_Synonym.concept_id, Concept_Synonym.language_concept_id\n",
-    "#         )\n",
-    "# )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ab83927f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(LabelMatch(input_label='heart attack', matched_label='Heart attack', concept_id=35819523, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=True, is_active=True),\n",
-       " LabelMatch(input_label='heart attack', matched_label='Heart attack', concept_id=36210764, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=True, is_active=True),\n",
-       " LabelMatch(input_label='heart attack', matched_label='Heart attack', concept_id=45466858, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True))"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kg.label_lookup(\"Heart attack\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "4592f774",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=4134723, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=True, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=3309601, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary Artery Thrombosis', concept_id=45921297, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=3161942, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=False),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=40623902, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=False)]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(\n",
-    "    kg.label_lookup(\"Coronary artery thrombosis\", fuzzy=False)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2d4a8b6f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=4134723, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=True, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Left anterior descending coronary artery thrombosis', concept_id=4153091, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=True, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Left main coronary artery thrombosis', concept_id=4209308, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=True, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Right main coronary artery thrombosis', concept_id=4304192, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=True, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Right main coronary artery thrombosis', concept_id=3169041, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Left main coronary artery thrombosis', concept_id=3270140, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=3309601, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Left anterior descending coronary artery thrombosis', concept_id=3403562, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary Artery Thrombosis Method Ident', concept_id=37528657, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Right Main Coronary Artery Thrombosis', concept_id=45908931, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Left Main Coronary Artery Thrombosis', concept_id=45915508, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary Artery Thrombosis', concept_id=45921297, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Left Anterior Descending Coronary Artery Thrombosis', concept_id=45955194, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=3161942, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=False),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=40623902, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=False)]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(\n",
-    "    kg.label_lookup(\"Coronary artery thrombosis\", fuzzy=True)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "7c964c57",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from omop_graph.reasoning.term_grounding import GroundingConstraints, ground_term\n",
-    "from omop_graph.reasoning.resolvers import ResolverPipeline, ExactLabelResolver, PartialLabelResolver, ExactSynonymResolver\n",
-    "from omop_graph.reasoning.term_grounding import _passes_constraints\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "69726c8e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "constraints = GroundingConstraints(\n",
-    "    parent_ids=(4027255,),#(37153816,),         # malignant neoplasm\n",
-    "    allowed_domains=(\"Condition\",), # redundant with parent_ids but perhaps we support combinations across domains eventually for more abstract grounding?\n",
-    "    allowed_vocabularies=(\"SNOMED\", \"ICD10CM\"),\n",
-    "    require_standard=True,\n",
-    "    max_depth=4,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "ec17c091",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resolver_pipeline = ResolverPipeline(\n",
-    "    resolvers=(ExactLabelResolver(),ExactSynonymResolver(),PartialLabelResolver()),\n",
-    "    stop_after_confidence=PartialLabelResolver.confidence\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "b2400dda",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[GroundingCandidate(concept_id=4329847, label='Myocardial infarction', best_path_profile=PathProfile(hops=2, invalid_concepts=0, non_standard_concepts=0, vocab_switches=0, ontological_edges=2, mapping_edges=0, metadata_edges=0), reasons=(), paths=(GraphPath(steps=(PathStep(subject=4329847, predicate='Is a', object=4185932), PathStep(subject=4185932, predicate='Is a', object=4027255))),))]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ground_term(kg, \"heart attack\", constraints=constraints, resolver_pipeline=resolver_pipeline)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "2bc5a61a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "e = ExactLabelResolver()\n",
-    "p = PartialLabelResolver()\n",
-    "s = ExactSynonymResolver()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "45a48125",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hits = e.resolve(kg, \"heart attack\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "8b45b6eb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Concept ID 35819523 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 36210764 passes: False, reasons: [\"domain Meas Value not in ('Condition',)\"]\n",
-      "Concept ID 45466858 passes: False, reasons: ['vocabulary Read not allowed']\n"
-     ]
-    }
-   ],
-   "source": [
-    "for h in hits:\n",
-    "    ok, reasons = _passes_constraints(kg, h.concept_id, constraints)\n",
-    "    print(f\"Concept ID {h.concept_id} passes: {ok}, reasons: {reasons}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "1dd00c08",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hits = s.resolve(kg, \"heart attack\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "85322723",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Concept ID 45616764 passes: False, reasons: ['vocabulary MeSH not allowed']\n",
-      "Concept ID 40398435 passes: False, reasons: ['concept is non-standard']\n",
-      "Concept ID 4329847 passes: True, reasons: []\n",
-      "Concept ID 40623902 passes: False, reasons: ['concept is non-standard']\n",
-      "Concept ID 4347016 passes: False, reasons: [\"domain Drug not in ('Condition',)\"]\n",
-      "Concept ID 40345176 passes: False, reasons: ['concept is non-standard']\n",
-      "Concept ID 40323863 passes: False, reasons: ['concept is non-standard']\n"
-     ]
-    }
-   ],
-   "source": [
-    "for h in hits:\n",
-    "    ok, reasons = _passes_constraints(kg, h.concept_id, constraints)\n",
-    "    print(f\"Concept ID {h.concept_id} passes: {ok}, reasons: {reasons}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "b4a7152a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 4329847 is the only concept that passes constraints and matches the selected grounding returned above"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c2c96ab4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kg-core (3.12.10)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/03_phenotype_helper.ipynb b/notebooks/03_phenotype_helper.ipynb
deleted file mode 100644
index 67f913b..0000000
--- a/notebooks/03_phenotype_helper.ipynb
+++ /dev/null
@@ -1,2319 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "1e074aa3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import sqlalchemy.orm as so\n",
-    "import sqlalchemy as sa\n",
-    "\n",
-    "from dataclasses import asdict\n",
-    "from dotenv import load_dotenv\n",
-    "from orm_loader.helpers import configure_logging\n",
-    "from omop_alchemy import get_engine_name\n",
-    "from omop_alchemy.cdm.model.vocabulary import Concept\n",
-    "\n",
-    "from omop_graph.graph.kg import KnowledgeGraph\n",
-    "from omop_graph.render import (\n",
-    "    bind_default_renderers,\n",
-    ")\n",
-    "from omop_graph.reasoning.phenotypes import (\n",
-    "    find_common_parents,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "339e4d2f",
-   "metadata": {},
-   "source": [
-    "At the moment, this demo is input to a manual process of hierarchy resolution, because there are certainly arguments for allowing impurity (e.g. SNOMED cancer conditions do not meet the original ICDO3-specific filter if there is no corresponding ICDO3 code), as well as for supporting less than full coverage (e.g. valid but unusual/atypical classifications that introduce a lot of noise) - TBD the ideal level of final automation here, but irrespectively it is is still relatively useful for initial orientation steps..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "33e36012",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2026-01-12 10:18:45,944 | INFO     | sql_loader.omop_alchemy.config | Database engine configured\n"
-     ]
-    }
-   ],
-   "source": [
-    "configure_logging()\n",
-    "load_dotenv()\n",
-    "\n",
-    "engine_string = get_engine_name()\n",
-    "engine = sa.create_engine(engine_string, future=True, echo=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "cb52ef35",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Session = so.sessionmaker(bind=engine)\n",
-    "\n",
-    "session = Session()\n",
-    "kg = KnowledgeGraph(session)\n",
-    "bind_default_renderers(kg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7e8f851d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seeds = [4173456, 4174251, 4174241, 4172043, 4173614, 4175026] # ecog 0 thru 5\n",
-    "parent_stats = find_common_parents(seeds=seeds, kg=kg)\n",
-    "candidate_df = pd.DataFrame([{'concept_id': c} | asdict(v) for c, v in parent_stats.items()])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "850d6df0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>concept_id</th>\n",
-       "      <th>descendants</th>\n",
-       "      <th>found</th>\n",
-       "      <th>coverage</th>\n",
-       "      <th>pollution</th>\n",
-       "      <th>completeness</th>\n",
-       "      <th>purity</th>\n",
-       "      <th>max_depth</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4308794</td>\n",
-       "      <td>{4174241, 4172043, 4174251, 4173614, 4173456, ...</td>\n",
-       "      <td>{4174241, 4172043, 4174251, 4173614, 4173456, ...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>432453</td>\n",
-       "      <td>{3655685, 4294662, 4032527, 4032528, 4032529, ...</td>\n",
-       "      <td>{4174241, 4172043, 4174251, 4173614, 4173456, ...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>986</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.006048</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4089214</td>\n",
-       "      <td>{46284803, 4153350, 4120591, 3661842, 3661843,...</td>\n",
-       "      <td>{4174241, 4172043, 4174251, 4173614, 4173456, ...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>4195</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.001428</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4041284</td>\n",
-       "      <td>{4259853, 4161577, 4161578, 4161579, 4128815, ...</td>\n",
-       "      <td>{4174241, 4172043, 4174251, 4173614, 4173456, ...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>7135</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.000840</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4094294</td>\n",
-       "      <td>{4259853, 4161577, 4161578, 4161579, 4227116, ...</td>\n",
-       "      <td>{4174241, 4172043, 4174251, 4173614, 4173456, ...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>14450</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.000415</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   concept_id                                        descendants  \\\n",
-       "0     4308794  {4174241, 4172043, 4174251, 4173614, 4173456, ...   \n",
-       "1      432453  {3655685, 4294662, 4032527, 4032528, 4032529, ...   \n",
-       "2     4089214  {46284803, 4153350, 4120591, 3661842, 3661843,...   \n",
-       "3     4041284  {4259853, 4161577, 4161578, 4161579, 4128815, ...   \n",
-       "4     4094294  {4259853, 4161577, 4161578, 4161579, 4227116, ...   \n",
-       "\n",
-       "                                               found  coverage  pollution  \\\n",
-       "0  {4174241, 4172043, 4174251, 4173614, 4173456, ...         6          0   \n",
-       "1  {4174241, 4172043, 4174251, 4173614, 4173456, ...         6        986   \n",
-       "2  {4174241, 4172043, 4174251, 4173614, 4173456, ...         6       4195   \n",
-       "3  {4174241, 4172043, 4174251, 4173614, 4173456, ...         6       7135   \n",
-       "4  {4174241, 4172043, 4174251, 4173614, 4173456, ...         6      14450   \n",
-       "\n",
-       "   completeness    purity  max_depth  \n",
-       "0           1.0  1.000000          1  \n",
-       "1           1.0  0.006048          2  \n",
-       "2           1.0  0.001428          5  \n",
-       "3           1.0  0.000840          5  \n",
-       "4           1.0  0.000415          4  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "candidate_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "1266d2fd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seeds = [4230556, 4085075, 434489, 4173456, 4174251, 4174241, 4172043, 4173614, 4175026] # alive, dead, at risk\n",
-    "parent_stats = find_common_parents(seeds=seeds, kg=kg)\n",
-    "candidate_df = pd.DataFrame([{'concept_id': c} | asdict(v) for c, v in parent_stats.items()])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "f6d165e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>concept_id</th>\n",
-       "      <th>descendants</th>\n",
-       "      <th>found</th>\n",
-       "      <th>coverage</th>\n",
-       "      <th>pollution</th>\n",
-       "      <th>completeness</th>\n",
-       "      <th>purity</th>\n",
-       "      <th>max_depth</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>432453</td>\n",
-       "      <td>{3655685, 4294662, 46270485, 35610650, 4202524...</td>\n",
-       "      <td>{4174241, 4172043, 4174251, 4173614, 4173456, ...</td>\n",
-       "      <td>9</td>\n",
-       "      <td>538</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.016453</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4308794</td>\n",
-       "      <td>{4174241, 4172043, 4174251, 4173614, 4173456, ...</td>\n",
-       "      <td>{4174241, 4172043, 4174251, 4173614, 4173456, ...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4089214</td>\n",
-       "      <td>{46284803, 4153350, 4120591, 3661842, 3661843,...</td>\n",
-       "      <td>{4174241, 4172043, 4174251, 4173614, 4173456, ...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>4195</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.001428</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4041284</td>\n",
-       "      <td>{4259853, 4161577, 4161578, 4161579, 4128815, ...</td>\n",
-       "      <td>{4174241, 4172043, 4174251, 4173614, 4173456, ...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>7135</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.000840</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4094294</td>\n",
-       "      <td>{4259853, 4161577, 4161578, 4161579, 4227116, ...</td>\n",
-       "      <td>{4174241, 4172043, 4174251, 4173614, 4173456, ...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>14450</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.000415</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   concept_id                                        descendants  \\\n",
-       "0      432453  {3655685, 4294662, 46270485, 35610650, 4202524...   \n",
-       "1     4308794  {4174241, 4172043, 4174251, 4173614, 4173456, ...   \n",
-       "2     4089214  {46284803, 4153350, 4120591, 3661842, 3661843,...   \n",
-       "3     4041284  {4259853, 4161577, 4161578, 4161579, 4128815, ...   \n",
-       "4     4094294  {4259853, 4161577, 4161578, 4161579, 4227116, ...   \n",
-       "\n",
-       "                                               found  coverage  pollution  \\\n",
-       "0  {4174241, 4172043, 4174251, 4173614, 4173456, ...         9        538   \n",
-       "1  {4174241, 4172043, 4174251, 4173614, 4173456, ...         6          0   \n",
-       "2  {4174241, 4172043, 4174251, 4173614, 4173456, ...         6       4195   \n",
-       "3  {4174241, 4172043, 4174251, 4173614, 4173456, ...         6       7135   \n",
-       "4  {4174241, 4172043, 4174251, 4173614, 4173456, ...         6      14450   \n",
-       "\n",
-       "   completeness    purity  max_depth  \n",
-       "0      1.000000  0.016453          2  \n",
-       "1      0.666667  1.000000          1  \n",
-       "2      0.666667  0.001428          5  \n",
-       "3      0.666667  0.000840          5  \n",
-       "4      0.666667  0.000415          4  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "candidate_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "3447f842",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "head_neck_conditions = [Concept.concept_code.ilike(f'%C{k:02d}%') for k in range(0, 15)]\n",
-    "head_neck_topog = [Concept.concept_code==f'C{k:02d}' for k in range(0, 15)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "159dafbd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "topog_concepts = pd.DataFrame(\n",
-    "    session.query(\n",
-    "        *Concept.__table__.columns\n",
-    "    )\n",
-    "    .filter(Concept.vocabulary_id=='ICDO3')\n",
-    "    .filter(sa.or_(*head_neck_topog))\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "64c9da45",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "cac82637",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hn_concepts = pd.DataFrame(\n",
-    "    session.query(\n",
-    "        *Concept.__table__.columns\n",
-    "    )\n",
-    "    .filter(Concept.vocabulary_id=='ICDO3')\n",
-    "    .filter(Concept.concept_class_id=='ICDO Condition')\n",
-    "    .filter(sa.or_(*head_neck_conditions))\n",
-    "    .filter(Concept.concept_code.like('%/3-%'))\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "87a0af78",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C00: Lip (734 candidates)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>concept_id</th>\n",
-       "      <th>descendants</th>\n",
-       "      <th>found</th>\n",
-       "      <th>coverage</th>\n",
-       "      <th>pollution</th>\n",
-       "      <th>completeness</th>\n",
-       "      <th>purity</th>\n",
-       "      <th>max_depth</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>159</th>\n",
-       "      <td>135858</td>\n",
-       "      <td>{36528135, 36519943, 36536330, 4145173, 365609...</td>\n",
-       "      <td>{36562945, 36532227, 36546565, 36517893, 36519...</td>\n",
-       "      <td>718</td>\n",
-       "      <td>775</td>\n",
-       "      <td>0.978202</td>\n",
-       "      <td>0.480911</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>136</th>\n",
-       "      <td>4116808</td>\n",
-       "      <td>{36528135, 36519943, 36536330, 36560921, 41779...</td>\n",
-       "      <td>{36562945, 36532227, 36546565, 36517893, 36519...</td>\n",
-       "      <td>718</td>\n",
-       "      <td>694</td>\n",
-       "      <td>0.978202</td>\n",
-       "      <td>0.508499</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>135</th>\n",
-       "      <td>37161350</td>\n",
-       "      <td>{36519943, 36528135, 36536330, 36560921, 36544...</td>\n",
-       "      <td>{36562945, 36532227, 36546565, 36517893, 36519...</td>\n",
-       "      <td>718</td>\n",
-       "      <td>520</td>\n",
-       "      <td>0.978202</td>\n",
-       "      <td>0.579968</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101</th>\n",
-       "      <td>4054605</td>\n",
-       "      <td>{602113, 4313090, 602115, 36532227, 602114, 36...</td>\n",
-       "      <td>{36562945, 36532227, 36546565, 36517893, 36519...</td>\n",
-       "      <td>718</td>\n",
-       "      <td>509</td>\n",
-       "      <td>0.978202</td>\n",
-       "      <td>0.585167</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>4180779</td>\n",
-       "      <td>{602113, 4313090, 602115, 36532227, 602114, 36...</td>\n",
-       "      <td>{36562945, 36532227, 36546565, 36517893, 36519...</td>\n",
-       "      <td>718</td>\n",
-       "      <td>240</td>\n",
-       "      <td>0.978202</td>\n",
-       "      <td>0.749478</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     concept_id                                        descendants  \\\n",
-       "159      135858  {36528135, 36519943, 36536330, 4145173, 365609...   \n",
-       "136     4116808  {36528135, 36519943, 36536330, 36560921, 41779...   \n",
-       "135    37161350  {36519943, 36528135, 36536330, 36560921, 36544...   \n",
-       "101     4054605  {602113, 4313090, 602115, 36532227, 602114, 36...   \n",
-       "54      4180779  {602113, 4313090, 602115, 36532227, 602114, 36...   \n",
-       "\n",
-       "                                                 found  coverage  pollution  \\\n",
-       "159  {36562945, 36532227, 36546565, 36517893, 36519...       718        775   \n",
-       "136  {36562945, 36532227, 36546565, 36517893, 36519...       718        694   \n",
-       "135  {36562945, 36532227, 36546565, 36517893, 36519...       718        520   \n",
-       "101  {36562945, 36532227, 36546565, 36517893, 36519...       718        509   \n",
-       "54   {36562945, 36532227, 36546565, 36517893, 36519...       718        240   \n",
-       "\n",
-       "     completeness    purity  max_depth  \n",
-       "159      0.978202  0.480911          7  \n",
-       "136      0.978202  0.508499          6  \n",
-       "135      0.978202  0.579968          6  \n",
-       "101      0.978202  0.585167          5  \n",
-       "54       0.978202  0.749478          4  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C01: Base of tongue (144 candidates)\n"
-     ]
-    },
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>concept_id</th>\n",
-       "      <th>descendants</th>\n",
-       "      <th>found</th>\n",
-       "      <th>coverage</th>\n",
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-       "      <th>purity</th>\n",
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-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>69</th>\n",
-       "      <td>4178962</td>\n",
-       "      <td>{36560896, 36528130, 36552707, 36524036, 36536...</td>\n",
-       "      <td>{36566017, 36562435, 36561419, 36555283, 36536...</td>\n",
-       "      <td>142</td>\n",
-       "      <td>1033</td>\n",
-       "      <td>0.986111</td>\n",
-       "      <td>0.120851</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>437498</td>\n",
-       "      <td>{36560896, 36528130, 36552707, 36536324, 36524...</td>\n",
-       "      <td>{36566017, 36562435, 36561419, 36555283, 36536...</td>\n",
-       "      <td>142</td>\n",
-       "      <td>985</td>\n",
-       "      <td>0.986111</td>\n",
-       "      <td>0.125998</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87</th>\n",
-       "      <td>4054607</td>\n",
-       "      <td>{36566017, 4248065, 36562435, 4158473, 3656141...</td>\n",
-       "      <td>{36566017, 36562435, 36561419, 36555283, 36536...</td>\n",
-       "      <td>142</td>\n",
-       "      <td>40</td>\n",
-       "      <td>0.986111</td>\n",
-       "      <td>0.780220</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>4181332</td>\n",
-       "      <td>{36566017, 4248065, 36562435, 4158473, 3656141...</td>\n",
-       "      <td>{36566017, 36562435, 36561419, 36555283, 36536...</td>\n",
-       "      <td>142</td>\n",
-       "      <td>13</td>\n",
-       "      <td>0.986111</td>\n",
-       "      <td>0.916129</td>\n",
-       "      <td>3</td>\n",
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-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>256633</td>\n",
-       "      <td>{36566017, 36562435, 36561419, 36555283, 36536...</td>\n",
-       "      <td>{36566017, 36562435, 36561419, 36555283, 36536...</td>\n",
-       "      <td>142</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.986111</td>\n",
-       "      <td>0.972603</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    concept_id                                        descendants  \\\n",
-       "69     4178962  {36560896, 36528130, 36552707, 36524036, 36536...   \n",
-       "33      437498  {36560896, 36528130, 36552707, 36536324, 36524...   \n",
-       "87     4054607  {36566017, 4248065, 36562435, 4158473, 3656141...   \n",
-       "34     4181332  {36566017, 4248065, 36562435, 4158473, 3656141...   \n",
-       "0       256633  {36566017, 36562435, 36561419, 36555283, 36536...   \n",
-       "\n",
-       "                                                found  coverage  pollution  \\\n",
-       "69  {36566017, 36562435, 36561419, 36555283, 36536...       142       1033   \n",
-       "33  {36566017, 36562435, 36561419, 36555283, 36536...       142        985   \n",
-       "87  {36566017, 36562435, 36561419, 36555283, 36536...       142         40   \n",
-       "34  {36566017, 36562435, 36561419, 36555283, 36536...       142         13   \n",
-       "0   {36566017, 36562435, 36561419, 36555283, 36536...       142          4   \n",
-       "\n",
-       "    completeness    purity  max_depth  \n",
-       "69      0.986111  0.120851          4  \n",
-       "33      0.986111  0.125998          3  \n",
-       "87      0.986111  0.780220          4  \n",
-       "34      0.986111  0.916129          3  \n",
-       "0       0.986111  0.972603          2  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C02: Other and unspecified parts of tongue (972 candidates)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>204</th>\n",
-       "      <td>432941</td>\n",
-       "      <td>{36560896, 36528130, 36552707, 36536324, 36519...</td>\n",
-       "      <td>{36560896, 36528130, 36552707, 36536324, 36524...</td>\n",
-       "      <td>962</td>\n",
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-       "      <td>0.989712</td>\n",
-       "      <td>0.634146</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>4103982</td>\n",
-       "      <td>{36560896, 36528130, 36552707, 36536324, 36519...</td>\n",
-       "      <td>{36560896, 36528130, 36552707, 36536324, 36524...</td>\n",
-       "      <td>962</td>\n",
-       "      <td>468</td>\n",
-       "      <td>0.989712</td>\n",
-       "      <td>0.672727</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>177</th>\n",
-       "      <td>40481371</td>\n",
-       "      <td>{36560896, 36528130, 36552707, 36536324, 36519...</td>\n",
-       "      <td>{36560896, 36528130, 36552707, 36536324, 36524...</td>\n",
-       "      <td>962</td>\n",
-       "      <td>414</td>\n",
-       "      <td>0.989712</td>\n",
-       "      <td>0.699128</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>139</th>\n",
-       "      <td>4130375</td>\n",
-       "      <td>{36560896, 36528130, 36552707, 36536324, 36519...</td>\n",
-       "      <td>{36560896, 36528130, 36552707, 36536324, 36524...</td>\n",
-       "      <td>962</td>\n",
-       "      <td>405</td>\n",
-       "      <td>0.989712</td>\n",
-       "      <td>0.703731</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>4178962</td>\n",
-       "      <td>{36560896, 36528130, 36552707, 36536324, 36524...</td>\n",
-       "      <td>{36560896, 36528130, 36552707, 36536324, 36524...</td>\n",
-       "      <td>962</td>\n",
-       "      <td>213</td>\n",
-       "      <td>0.989712</td>\n",
-       "      <td>0.818723</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     concept_id                                        descendants  \\\n",
-       "204      432941  {36560896, 36528130, 36552707, 36536324, 36519...   \n",
-       "176     4103982  {36560896, 36528130, 36552707, 36536324, 36519...   \n",
-       "177    40481371  {36560896, 36528130, 36552707, 36536324, 36519...   \n",
-       "139     4130375  {36560896, 36528130, 36552707, 36536324, 36519...   \n",
-       "85      4178962  {36560896, 36528130, 36552707, 36536324, 36524...   \n",
-       "\n",
-       "                                                 found  coverage  pollution  \\\n",
-       "204  {36560896, 36528130, 36552707, 36536324, 36524...       962        555   \n",
-       "176  {36560896, 36528130, 36552707, 36536324, 36524...       962        468   \n",
-       "177  {36560896, 36528130, 36552707, 36536324, 36524...       962        414   \n",
-       "139  {36560896, 36528130, 36552707, 36536324, 36524...       962        405   \n",
-       "85   {36560896, 36528130, 36552707, 36536324, 36524...       962        213   \n",
-       "\n",
-       "     completeness    purity  max_depth  \n",
-       "204      0.989712  0.634146          8  \n",
-       "176      0.989712  0.672727          7  \n",
-       "177      0.989712  0.699128          7  \n",
-       "139      0.989712  0.703731          6  \n",
-       "85       0.989712  0.818723          5  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C03: Gum (456 candidates)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "      <th></th>\n",
-       "      <th>concept_id</th>\n",
-       "      <th>descendants</th>\n",
-       "      <th>found</th>\n",
-       "      <th>coverage</th>\n",
-       "      <th>pollution</th>\n",
-       "      <th>completeness</th>\n",
-       "      <th>purity</th>\n",
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-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>4246124</td>\n",
-       "      <td>{36546563, 36540420, 36548612, 602118, 602126,...</td>\n",
-       "      <td>{36540420, 36548612, 36558862, 36544539, 44503...</td>\n",
-       "      <td>450</td>\n",
-       "      <td>165</td>\n",
-       "      <td>0.986842</td>\n",
-       "      <td>0.731707</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>154</th>\n",
-       "      <td>37163090</td>\n",
-       "      <td>{36548612, 36540420, 36548614, 602118, 602126,...</td>\n",
-       "      <td>{36548612, 36540420, 36558862, 36544539, 44503...</td>\n",
-       "      <td>450</td>\n",
-       "      <td>130</td>\n",
-       "      <td>0.986842</td>\n",
-       "      <td>0.775862</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>129</th>\n",
-       "      <td>4054612</td>\n",
-       "      <td>{36548612, 36540420, 36548614, 602118, 3655886...</td>\n",
-       "      <td>{36548612, 36540420, 36558862, 36544539, 44503...</td>\n",
-       "      <td>450</td>\n",
-       "      <td>112</td>\n",
-       "      <td>0.986842</td>\n",
-       "      <td>0.800712</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>4178963</td>\n",
-       "      <td>{36548612, 36540420, 602118, 602126, 36558862,...</td>\n",
-       "      <td>{36548612, 36540420, 36558862, 36544539, 44503...</td>\n",
-       "      <td>450</td>\n",
-       "      <td>26</td>\n",
-       "      <td>0.986842</td>\n",
-       "      <td>0.945378</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>140950</td>\n",
-       "      <td>{36540420, 36548612, 602118, 36558862, 440335,...</td>\n",
-       "      <td>{36540420, 36548612, 36558862, 36544539, 44503...</td>\n",
-       "      <td>450</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0.986842</td>\n",
-       "      <td>0.978261</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     concept_id                                        descendants  \\\n",
-       "84      4246124  {36546563, 36540420, 36548612, 602118, 602126,...   \n",
-       "154    37163090  {36548612, 36540420, 36548614, 602118, 602126,...   \n",
-       "129     4054612  {36548612, 36540420, 36548614, 602118, 3655886...   \n",
-       "82      4178963  {36548612, 36540420, 602118, 602126, 36558862,...   \n",
-       "6        140950  {36540420, 36548612, 602118, 36558862, 440335,...   \n",
-       "\n",
-       "                                                 found  coverage  pollution  \\\n",
-       "84   {36540420, 36548612, 36558862, 36544539, 44503...       450        165   \n",
-       "154  {36548612, 36540420, 36558862, 36544539, 44503...       450        130   \n",
-       "129  {36548612, 36540420, 36558862, 36544539, 44503...       450        112   \n",
-       "82   {36548612, 36540420, 36558862, 36544539, 44503...       450         26   \n",
-       "6    {36540420, 36548612, 36558862, 36544539, 44503...       450         10   \n",
-       "\n",
-       "     completeness    purity  max_depth  \n",
-       "84       0.986842  0.731707          3  \n",
-       "154      0.986842  0.775862          5  \n",
-       "129      0.986842  0.800712          4  \n",
-       "82       0.986842  0.945378          3  \n",
-       "6        0.986842  0.978261          2  "
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-      "C04: Floor of mouth (608 candidates)\n"
-     ]
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>4247357</td>\n",
-       "      <td>{36552704, 36560896, 36528130, 36552707, 36536...</td>\n",
-       "      <td>{36556800, 36521986, 44498948, 36536325, 36556...</td>\n",
-       "      <td>601</td>\n",
-       "      <td>3795</td>\n",
-       "      <td>0.988487</td>\n",
-       "      <td>0.136715</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>439404</td>\n",
-       "      <td>{36552704, 36560896, 36528130, 36552707, 36536...</td>\n",
-       "      <td>{36556800, 36521986, 44498948, 36536325, 36556...</td>\n",
-       "      <td>601</td>\n",
-       "      <td>3626</td>\n",
-       "      <td>0.988487</td>\n",
-       "      <td>0.142181</td>\n",
-       "      <td>3</td>\n",
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-       "    <tr>\n",
-       "      <th>121</th>\n",
-       "      <td>4130381</td>\n",
-       "      <td>{36556800, 36521986, 44498948, 36536325, 36556...</td>\n",
-       "      <td>{36556800, 36521986, 44498948, 36536325, 36556...</td>\n",
-       "      <td>601</td>\n",
-       "      <td>321</td>\n",
-       "      <td>0.988487</td>\n",
-       "      <td>0.651844</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <th>76</th>\n",
-       "      <td>4177101</td>\n",
-       "      <td>{36556800, 36521986, 44498948, 36536325, 36556...</td>\n",
-       "      <td>{36556800, 36521986, 44498948, 36536325, 36556...</td>\n",
-       "      <td>601</td>\n",
-       "      <td>201</td>\n",
-       "      <td>0.988487</td>\n",
-       "      <td>0.749377</td>\n",
-       "      <td>3</td>\n",
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-       "      <th>0</th>\n",
-       "      <td>135750</td>\n",
-       "      <td>{36556800, 36521986, 44498948, 36536325, 36556...</td>\n",
-       "      <td>{36556800, 36521986, 44498948, 36536325, 36556...</td>\n",
-       "      <td>601</td>\n",
-       "      <td>179</td>\n",
-       "      <td>0.988487</td>\n",
-       "      <td>0.770513</td>\n",
-       "      <td>2</td>\n",
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-       "     concept_id                                        descendants  \\\n",
-       "74      4247357  {36552704, 36560896, 36528130, 36552707, 36536...   \n",
-       "75       439404  {36552704, 36560896, 36528130, 36552707, 36536...   \n",
-       "121     4130381  {36556800, 36521986, 44498948, 36536325, 36556...   \n",
-       "76      4177101  {36556800, 36521986, 44498948, 36536325, 36556...   \n",
-       "0        135750  {36556800, 36521986, 44498948, 36536325, 36556...   \n",
-       "\n",
-       "                                                 found  coverage  pollution  \\\n",
-       "74   {36556800, 36521986, 44498948, 36536325, 36556...       601       3795   \n",
-       "75   {36556800, 36521986, 44498948, 36536325, 36556...       601       3626   \n",
-       "121  {36556800, 36521986, 44498948, 36536325, 36556...       601        321   \n",
-       "76   {36556800, 36521986, 44498948, 36536325, 36556...       601        201   \n",
-       "0    {36556800, 36521986, 44498948, 36536325, 36556...       601        179   \n",
-       "\n",
-       "     completeness    purity  max_depth  \n",
-       "74       0.988487  0.136715          3  \n",
-       "75       0.988487  0.142181          3  \n",
-       "121      0.988487  0.651844          4  \n",
-       "76       0.988487  0.749377          3  \n",
-       "0        0.988487  0.770513          2  "
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-     "output_type": "stream",
-     "text": [
-      "C05: Palate (762 candidates)\n"
-     ]
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-       "      <th>concept_id</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>166</th>\n",
-       "      <td>4091365</td>\n",
-       "      <td>{602112, 36562944, 42512387, 45768712, 3653018...</td>\n",
-       "      <td>{42512387, 36530184, 44501004, 36550670, 36524...</td>\n",
-       "      <td>751</td>\n",
-       "      <td>448</td>\n",
-       "      <td>0.985564</td>\n",
-       "      <td>0.626355</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>150</th>\n",
-       "      <td>4028251</td>\n",
-       "      <td>{602112, 36562944, 42512387, 45768712, 3653018...</td>\n",
-       "      <td>{42512387, 36530184, 44501004, 36550670, 36524...</td>\n",
-       "      <td>751</td>\n",
-       "      <td>424</td>\n",
-       "      <td>0.985564</td>\n",
-       "      <td>0.639149</td>\n",
-       "      <td>6</td>\n",
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-       "    <tr>\n",
-       "      <th>151</th>\n",
-       "      <td>37162644</td>\n",
-       "      <td>{602112, 36562944, 42512387, 36530184, 602123,...</td>\n",
-       "      <td>{42512387, 36530184, 44501004, 36550670, 36524...</td>\n",
-       "      <td>751</td>\n",
-       "      <td>180</td>\n",
-       "      <td>0.985564</td>\n",
-       "      <td>0.806660</td>\n",
-       "      <td>6</td>\n",
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-       "    <tr>\n",
-       "      <th>126</th>\n",
-       "      <td>4131604</td>\n",
-       "      <td>{602112, 36562944, 42512387, 36530184, 602123,...</td>\n",
-       "      <td>{42512387, 36530184, 44501004, 36550670, 36524...</td>\n",
-       "      <td>751</td>\n",
-       "      <td>166</td>\n",
-       "      <td>0.985564</td>\n",
-       "      <td>0.818975</td>\n",
-       "      <td>5</td>\n",
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-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>4178964</td>\n",
-       "      <td>{602112, 42512387, 36530184, 602123, 44501004,...</td>\n",
-       "      <td>{42512387, 36530184, 44501004, 36550670, 36524...</td>\n",
-       "      <td>751</td>\n",
-       "      <td>40</td>\n",
-       "      <td>0.985564</td>\n",
-       "      <td>0.949431</td>\n",
-       "      <td>4</td>\n",
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-       "     concept_id                                        descendants  \\\n",
-       "166     4091365  {602112, 36562944, 42512387, 45768712, 3653018...   \n",
-       "150     4028251  {602112, 36562944, 42512387, 45768712, 3653018...   \n",
-       "151    37162644  {602112, 36562944, 42512387, 36530184, 602123,...   \n",
-       "126     4131604  {602112, 36562944, 42512387, 36530184, 602123,...   \n",
-       "85      4178964  {602112, 42512387, 36530184, 602123, 44501004,...   \n",
-       "\n",
-       "                                                 found  coverage  pollution  \\\n",
-       "166  {42512387, 36530184, 44501004, 36550670, 36524...       751        448   \n",
-       "150  {42512387, 36530184, 44501004, 36550670, 36524...       751        424   \n",
-       "151  {42512387, 36530184, 44501004, 36550670, 36524...       751        180   \n",
-       "126  {42512387, 36530184, 44501004, 36550670, 36524...       751        166   \n",
-       "85   {42512387, 36530184, 44501004, 36550670, 36524...       751         40   \n",
-       "\n",
-       "     completeness    purity  max_depth  \n",
-       "166      0.985564  0.626355          7  \n",
-       "150      0.985564  0.639149          6  \n",
-       "151      0.985564  0.806660          6  \n",
-       "126      0.985564  0.818975          5  \n",
-       "85       0.985564  0.949431          4  "
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-     "output_type": "stream",
-     "text": [
-      "C06: Other and unspecified parts of mouth (761 candidates)\n"
-     ]
-    },
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-       "      <th>concept_id</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>177</th>\n",
-       "      <td>4307122</td>\n",
-       "      <td>{36536324, 44498948, 36536325, 44498950, 36536...</td>\n",
-       "      <td>{36552704, 36538370, 44500996, 36540422, 36560...</td>\n",
-       "      <td>754</td>\n",
-       "      <td>8475</td>\n",
-       "      <td>0.990802</td>\n",
-       "      <td>0.081699</td>\n",
-       "      <td>8</td>\n",
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-       "    <tr>\n",
-       "      <th>150</th>\n",
-       "      <td>4042502</td>\n",
-       "      <td>{36536324, 44498948, 36536325, 44498950, 36536...</td>\n",
-       "      <td>{36552704, 36538370, 44500996, 36540422, 36560...</td>\n",
-       "      <td>754</td>\n",
-       "      <td>7978</td>\n",
-       "      <td>0.990802</td>\n",
-       "      <td>0.086349</td>\n",
-       "      <td>7</td>\n",
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-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>43530620</td>\n",
-       "      <td>{36536324, 44498948, 36536325, 44498950, 36536...</td>\n",
-       "      <td>{36552704, 36538370, 44500996, 36540422, 36560...</td>\n",
-       "      <td>754</td>\n",
-       "      <td>6399</td>\n",
-       "      <td>0.990802</td>\n",
-       "      <td>0.105410</td>\n",
-       "      <td>6</td>\n",
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-       "    <tr>\n",
-       "      <th>192</th>\n",
-       "      <td>4184252</td>\n",
-       "      <td>{44498945, 4096016, 4194323, 36536341, 4449896...</td>\n",
-       "      <td>{36552704, 36538370, 44500996, 36540422, 36560...</td>\n",
-       "      <td>754</td>\n",
-       "      <td>6023</td>\n",
-       "      <td>0.990802</td>\n",
-       "      <td>0.111259</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>4130986</td>\n",
-       "      <td>{36536324, 44498948, 36536325, 44498950, 36536...</td>\n",
-       "      <td>{36552704, 36538370, 44500996, 36540422, 36560...</td>\n",
-       "      <td>754</td>\n",
-       "      <td>5376</td>\n",
-       "      <td>0.990802</td>\n",
-       "      <td>0.123002</td>\n",
-       "      <td>5</td>\n",
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-       "     concept_id                                        descendants  \\\n",
-       "177     4307122  {36536324, 44498948, 36536325, 44498950, 36536...   \n",
-       "150     4042502  {36536324, 44498948, 36536325, 44498950, 36536...   \n",
-       "93     43530620  {36536324, 44498948, 36536325, 44498950, 36536...   \n",
-       "192     4184252  {44498945, 4096016, 4194323, 36536341, 4449896...   \n",
-       "6       4130986  {36536324, 44498948, 36536325, 44498950, 36536...   \n",
-       "\n",
-       "                                                 found  coverage  pollution  \\\n",
-       "177  {36552704, 36538370, 44500996, 36540422, 36560...       754       8475   \n",
-       "150  {36552704, 36538370, 44500996, 36540422, 36560...       754       7978   \n",
-       "93   {36552704, 36538370, 44500996, 36540422, 36560...       754       6399   \n",
-       "192  {36552704, 36538370, 44500996, 36540422, 36560...       754       6023   \n",
-       "6    {36552704, 36538370, 44500996, 36540422, 36560...       754       5376   \n",
-       "\n",
-       "     completeness    purity  max_depth  \n",
-       "177      0.990802  0.081699          8  \n",
-       "150      0.990802  0.086349          7  \n",
-       "93       0.990802  0.105410          6  \n",
-       "192      0.990802  0.111259          7  \n",
-       "6        0.990802  0.123002          5  "
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-     "output_type": "stream",
-     "text": [
-      "C07: Parotid glanid (191 candidates)\n"
-     ]
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-       "      <th>concept_id</th>\n",
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-       "      <th>found</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>108</th>\n",
-       "      <td>4322566</td>\n",
-       "      <td>{44498950, 42512393, 36521996, 4096013, 365445...</td>\n",
-       "      <td>{42512896, 42511876, 44498950, 36544007, 44500...</td>\n",
-       "      <td>185</td>\n",
-       "      <td>928</td>\n",
-       "      <td>0.968586</td>\n",
-       "      <td>0.166217</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>90</th>\n",
-       "      <td>4122227</td>\n",
-       "      <td>{44498950, 42512393, 36521996, 36544530, 44498...</td>\n",
-       "      <td>{42512896, 42511876, 44498950, 36544007, 44500...</td>\n",
-       "      <td>185</td>\n",
-       "      <td>902</td>\n",
-       "      <td>0.968586</td>\n",
-       "      <td>0.170193</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>91</th>\n",
-       "      <td>4054610</td>\n",
-       "      <td>{44498950, 42512393, 36521996, 36544530, 44498...</td>\n",
-       "      <td>{42512896, 42511876, 44498950, 36544007, 44500...</td>\n",
-       "      <td>185</td>\n",
-       "      <td>862</td>\n",
-       "      <td>0.968586</td>\n",
-       "      <td>0.176695</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>4202556</td>\n",
-       "      <td>{42512896, 4025347, 42511876, 44498950, 365440...</td>\n",
-       "      <td>{42512896, 42511876, 44498950, 36544007, 44500...</td>\n",
-       "      <td>185</td>\n",
-       "      <td>58</td>\n",
-       "      <td>0.968586</td>\n",
-       "      <td>0.761317</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>68</th>\n",
-       "      <td>4130984</td>\n",
-       "      <td>{42512896, 42511876, 44498950, 36544007, 44500...</td>\n",
-       "      <td>{42512896, 42511876, 44498950, 36544007, 44500...</td>\n",
-       "      <td>185</td>\n",
-       "      <td>48</td>\n",
-       "      <td>0.968586</td>\n",
-       "      <td>0.793991</td>\n",
-       "      <td>4</td>\n",
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-       "  </tbody>\n",
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-       "     concept_id                                        descendants  \\\n",
-       "108     4322566  {44498950, 42512393, 36521996, 4096013, 365445...   \n",
-       "90      4122227  {44498950, 42512393, 36521996, 36544530, 44498...   \n",
-       "91      4054610  {44498950, 42512393, 36521996, 36544530, 44498...   \n",
-       "92      4202556  {42512896, 4025347, 42511876, 44498950, 365440...   \n",
-       "68      4130984  {42512896, 42511876, 44498950, 36544007, 44500...   \n",
-       "\n",
-       "                                                 found  coverage  pollution  \\\n",
-       "108  {42512896, 42511876, 44498950, 36544007, 44500...       185        928   \n",
-       "90   {42512896, 42511876, 44498950, 36544007, 44500...       185        902   \n",
-       "91   {42512896, 42511876, 44498950, 36544007, 44500...       185        862   \n",
-       "92   {42512896, 42511876, 44498950, 36544007, 44500...       185         58   \n",
-       "68   {42512896, 42511876, 44498950, 36544007, 44500...       185         48   \n",
-       "\n",
-       "     completeness    purity  max_depth  \n",
-       "108      0.968586  0.166217          7  \n",
-       "90       0.968586  0.170193          6  \n",
-       "91       0.968586  0.176695          5  \n",
-       "92       0.968586  0.761317          5  \n",
-       "68       0.968586  0.793991          4  "
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-      "C08: Other and unspecified major salivary glands (699 candidates)\n"
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-       "      <th>196</th>\n",
-       "      <td>25572</td>\n",
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-       "      <th>177</th>\n",
-       "      <td>4114483</td>\n",
-       "      <td>{44498950, 42512393, 36521996, 4096013, 409601...</td>\n",
-       "      <td>{36544530, 36562967, 36562968, 36528152, 36567...</td>\n",
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-       "    <tr>\n",
-       "      <th>178</th>\n",
-       "      <td>4322566</td>\n",
-       "      <td>{44498950, 42512393, 36521996, 4096013, 365445...</td>\n",
-       "      <td>{36544530, 36562967, 36562968, 36528152, 36567...</td>\n",
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-       "      <th>155</th>\n",
-       "      <td>4122227</td>\n",
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-       "      <td>{36544530, 36562967, 36562968, 36528152, 36567...</td>\n",
-       "      <td>688</td>\n",
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-       "      <th>130</th>\n",
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-       "      <td>{36544530, 36562967, 36562968, 36528152, 36567...</td>\n",
-       "      <td>688</td>\n",
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-       "      <td>0.657116</td>\n",
-       "      <td>4</td>\n",
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-       "     concept_id                                        descendants  \\\n",
-       "196       25572  {44498950, 42512393, 36521996, 4096013, 409601...   \n",
-       "177     4114483  {44498950, 42512393, 36521996, 4096013, 409601...   \n",
-       "178     4322566  {44498950, 42512393, 36521996, 4096013, 365445...   \n",
-       "155     4122227  {44498950, 42512393, 36521996, 36544530, 44498...   \n",
-       "130     4054610  {44498950, 42512393, 36521996, 36544530, 44498...   \n",
-       "\n",
-       "                                                 found  coverage  pollution  \\\n",
-       "196  {36544530, 36562967, 36562968, 36528152, 36567...       688        526   \n",
-       "177  {36544530, 36562967, 36562968, 36528152, 36567...       688        441   \n",
-       "178  {36544530, 36562967, 36562968, 36528152, 36567...       688        425   \n",
-       "155  {36544530, 36562967, 36562968, 36528152, 36567...       688        399   \n",
-       "130  {36544530, 36562967, 36562968, 36528152, 36567...       688        359   \n",
-       "\n",
-       "     completeness    purity  max_depth  \n",
-       "196      0.984263  0.566722          7  \n",
-       "177      0.984263  0.609389          6  \n",
-       "178      0.984263  0.618149          6  \n",
-       "155      0.984263  0.632935          5  \n",
-       "130      0.984263  0.657116          4  "
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-     "output_type": "stream",
-     "text": [
-      "C09: Tonsil (673 candidates)\n"
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-       "      <th>204</th>\n",
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-       "      <td>46270500</td>\n",
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-       "      <th>205</th>\n",
-       "      <td>4054617</td>\n",
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-       "      <td>{36519941, 36562956, 36524045, 36530193, 36532...</td>\n",
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-       "      <td>0.330193</td>\n",
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-       "      <th>117</th>\n",
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-       "     concept_id                                        descendants  \\\n",
-       "204     4177243  {44498945, 36560900, 36519941, 36528141, 36560...   \n",
-       "273     4054510  {36519941, 36560915, 36536342, 36560929, 41779...   \n",
-       "241    46270500  {36519941, 36560915, 36536342, 36560929, 36519...   \n",
-       "205     4054617  {36519941, 36560915, 36536342, 36560929, 36519...   \n",
-       "117     4181338  {36519941, 36560915, 36536342, 36560929, 36519...   \n",
-       "\n",
-       "                                                 found  coverage  pollution  \\\n",
-       "204  {36519941, 36562956, 36524045, 36530193, 36532...       666       3389   \n",
-       "273  {36519941, 36562956, 36524045, 36530193, 36532...       666       1463   \n",
-       "241  {36519941, 36562956, 36524045, 36530193, 36532...       666       1406   \n",
-       "205  {36519941, 36562956, 36524045, 36530193, 36532...       666       1351   \n",
-       "117  {36519941, 36562956, 36524045, 36530193, 36532...       666       1103   \n",
-       "\n",
-       "     completeness    purity  max_depth  \n",
-       "204      0.989599  0.164242          7  \n",
-       "273      0.989599  0.312823          7  \n",
-       "241      0.989599  0.321429          7  \n",
-       "205      0.989599  0.330193          6  \n",
-       "117      0.989599  0.376484          6  "
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-      "C10: Oropharynx (1174 candidates)\n"
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-       "      <th>123</th>\n",
-       "      <td>4274025</td>\n",
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-       "      <th>124</th>\n",
-       "      <td>4102111</td>\n",
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-       "      <td>0.016378</td>\n",
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-       "      <th>223</th>\n",
-       "      <td>4266186</td>\n",
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-       "      <th>1</th>\n",
-       "      <td>438112</td>\n",
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-       "      <td>{36556803, 36554756, 36562951, 36558856, 36540...</td>\n",
-       "      <td>1145</td>\n",
-       "      <td>68127</td>\n",
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-       "      <td>10</td>\n",
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-       "      <th>122</th>\n",
-       "      <td>443392</td>\n",
-       "      <td>{36524322, 42598456, 36559370, 36403071, 42598...</td>\n",
-       "      <td>{36556803, 36554756, 36562951, 36558856, 36540...</td>\n",
-       "      <td>1145</td>\n",
-       "      <td>55071</td>\n",
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-       "      <td>0.020368</td>\n",
-       "      <td>9</td>\n",
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-       "     concept_id                                        descendants  \\\n",
-       "123     4274025  {4194320, 4194323, 4194328, 4194355, 4194365, ...   \n",
-       "124     4102111  {36522894, 36403062, 36403063, 42598455, 42598...   \n",
-       "223     4266186  {36403062, 36403063, 42598455, 42598456, 42598...   \n",
-       "1        438112  {36403062, 36403063, 42598456, 42598461, 36403...   \n",
-       "122      443392  {36524322, 42598456, 36559370, 36403071, 42598...   \n",
-       "\n",
-       "                                                 found  coverage  pollution  \\\n",
-       "123  {36556803, 36554756, 36562951, 36558856, 36540...      1145     165228   \n",
-       "124  {36556803, 36554756, 36562951, 36558856, 36540...      1145      68764   \n",
-       "223  {36556803, 36554756, 36562951, 36558856, 36540...      1145      68495   \n",
-       "1    {36556803, 36554756, 36562951, 36558856, 36540...      1145      68127   \n",
-       "122  {36556803, 36554756, 36562951, 36558856, 36540...      1145      55071   \n",
-       "\n",
-       "     completeness    purity  max_depth  \n",
-       "123      0.975298  0.006882         12  \n",
-       "124      0.975298  0.016378         11  \n",
-       "223      0.975298  0.016442          9  \n",
-       "1        0.975298  0.016529         10  \n",
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-      "C11: Nasopharynx (976 candidates)\n"
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-       "      <th>187</th>\n",
-       "      <td>435836</td>\n",
-       "      <td>{44503040, 36526083, 36560900, 36524041, 60212...</td>\n",
-       "      <td>{44503040, 36526083, 36560900, 36524041, 36542...</td>\n",
-       "      <td>961</td>\n",
-       "      <td>245</td>\n",
-       "      <td>0.984631</td>\n",
-       "      <td>0.796849</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>165</th>\n",
-       "      <td>44783160</td>\n",
-       "      <td>{44503040, 36526083, 36560900, 36524041, 60212...</td>\n",
-       "      <td>{44503040, 36526083, 36560900, 36524041, 36542...</td>\n",
-       "      <td>961</td>\n",
-       "      <td>211</td>\n",
-       "      <td>0.984631</td>\n",
-       "      <td>0.819966</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>164</th>\n",
-       "      <td>4181960</td>\n",
-       "      <td>{44503040, 36526083, 36560900, 36524041, 60212...</td>\n",
-       "      <td>{44503040, 36526083, 36560900, 36524041, 36542...</td>\n",
-       "      <td>961</td>\n",
-       "      <td>195</td>\n",
-       "      <td>0.984631</td>\n",
-       "      <td>0.831315</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>136</th>\n",
-       "      <td>4054514</td>\n",
-       "      <td>{44503040, 36526083, 36560900, 36524041, 60212...</td>\n",
-       "      <td>{44503040, 36526083, 36560900, 36524041, 36542...</td>\n",
-       "      <td>961</td>\n",
-       "      <td>185</td>\n",
-       "      <td>0.984631</td>\n",
-       "      <td>0.838569</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>4095312</td>\n",
-       "      <td>{44503040, 36526083, 36560900, 36524041, 60212...</td>\n",
-       "      <td>{44503040, 36526083, 36560900, 36524041, 36542...</td>\n",
-       "      <td>961</td>\n",
-       "      <td>70</td>\n",
-       "      <td>0.984631</td>\n",
-       "      <td>0.932105</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     concept_id                                        descendants  \\\n",
-       "187      435836  {44503040, 36526083, 36560900, 36524041, 60212...   \n",
-       "165    44783160  {44503040, 36526083, 36560900, 36524041, 60212...   \n",
-       "164     4181960  {44503040, 36526083, 36560900, 36524041, 60212...   \n",
-       "136     4054514  {44503040, 36526083, 36560900, 36524041, 60212...   \n",
-       "96      4095312  {44503040, 36526083, 36560900, 36524041, 60212...   \n",
-       "\n",
-       "                                                 found  coverage  pollution  \\\n",
-       "187  {44503040, 36526083, 36560900, 36524041, 36542...       961        245   \n",
-       "165  {44503040, 36526083, 36560900, 36524041, 36542...       961        211   \n",
-       "164  {44503040, 36526083, 36560900, 36524041, 36542...       961        195   \n",
-       "136  {44503040, 36526083, 36560900, 36524041, 36542...       961        185   \n",
-       "96   {44503040, 36526083, 36560900, 36524041, 36542...       961         70   \n",
-       "\n",
-       "     completeness    purity  max_depth  \n",
-       "187      0.984631  0.796849          8  \n",
-       "165      0.984631  0.819966          7  \n",
-       "164      0.984631  0.831315          7  \n",
-       "136      0.984631  0.838569          6  \n",
-       "96       0.984631  0.932105          5  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C12: Pyriform sinus (134 candidates)\n"
-     ]
-    },
-    {
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>4181342</td>\n",
-       "      <td>{44498945, 36534274, 36558851, 36517896, 36556...</td>\n",
-       "      <td>{44498945, 36567557, 36567567, 36539926, 36534...</td>\n",
-       "      <td>133</td>\n",
-       "      <td>710</td>\n",
-       "      <td>0.992537</td>\n",
-       "      <td>0.157770</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>439746</td>\n",
-       "      <td>{44498945, 36534274, 36558851, 36517896, 36556...</td>\n",
-       "      <td>{44498945, 36567557, 36567567, 36539926, 36534...</td>\n",
-       "      <td>133</td>\n",
-       "      <td>676</td>\n",
-       "      <td>0.992537</td>\n",
-       "      <td>0.164400</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>4129252</td>\n",
-       "      <td>{44498945, 36560901, 36567557, 36567567, 43156...</td>\n",
-       "      <td>{44498945, 36567557, 36567567, 36539926, 36534...</td>\n",
-       "      <td>133</td>\n",
-       "      <td>31</td>\n",
-       "      <td>0.992537</td>\n",
-       "      <td>0.810976</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>4180789</td>\n",
-       "      <td>{44498945, 36567557, 36567567, 4315668, 365399...</td>\n",
-       "      <td>{44498945, 36567557, 36567567, 36539926, 36534...</td>\n",
-       "      <td>133</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.992537</td>\n",
-       "      <td>0.963768</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>435190</td>\n",
-       "      <td>{44498945, 36567557, 36567567, 36539926, 36534...</td>\n",
-       "      <td>{44498945, 36567557, 36567567, 36539926, 36534...</td>\n",
-       "      <td>133</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.992537</td>\n",
-       "      <td>0.992537</td>\n",
-       "      <td>2</td>\n",
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-       "    concept_id                                        descendants  \\\n",
-       "46     4181342  {44498945, 36534274, 36558851, 36517896, 36556...   \n",
-       "31      439746  {44498945, 36534274, 36558851, 36517896, 36556...   \n",
-       "38     4129252  {44498945, 36560901, 36567557, 36567567, 43156...   \n",
-       "32     4180789  {44498945, 36567557, 36567567, 4315668, 365399...   \n",
-       "1       435190  {44498945, 36567557, 36567567, 36539926, 36534...   \n",
-       "\n",
-       "                                                found  coverage  pollution  \\\n",
-       "46  {44498945, 36567557, 36567567, 36539926, 36534...       133        710   \n",
-       "31  {44498945, 36567557, 36567567, 36539926, 36534...       133        676   \n",
-       "38  {44498945, 36567557, 36567567, 36539926, 36534...       133         31   \n",
-       "32  {44498945, 36567557, 36567567, 36539926, 36534...       133          5   \n",
-       "1   {44498945, 36567557, 36567567, 36539926, 36534...       133          1   \n",
-       "\n",
-       "    completeness    purity  max_depth  \n",
-       "46      0.992537  0.157770          5  \n",
-       "31      0.992537  0.164400          4  \n",
-       "38      0.992537  0.810976          4  \n",
-       "32      0.992537  0.963768          4  \n",
-       "1       0.992537  0.992537          2  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C13: Hypopharynx (669 candidates)\n"
-     ]
-    },
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-       "  <tbody>\n",
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-       "      <th>249</th>\n",
-       "      <td>4129247</td>\n",
-       "      <td>{44498945, 36534274, 36558851, 36560901, 36517...</td>\n",
-       "      <td>{36534274, 36558851, 36517896, 36556809, 36548...</td>\n",
-       "      <td>651</td>\n",
-       "      <td>378</td>\n",
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-       "      <td>0.632653</td>\n",
-       "      <td>8</td>\n",
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-       "    <tr>\n",
-       "      <th>212</th>\n",
-       "      <td>44783159</td>\n",
-       "      <td>{44498945, 36534274, 36558851, 36560901, 36517...</td>\n",
-       "      <td>{36534274, 36558851, 36517896, 36556809, 36548...</td>\n",
-       "      <td>651</td>\n",
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-       "      <td>0.640118</td>\n",
-       "      <td>8</td>\n",
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-       "    <tr>\n",
-       "      <th>154</th>\n",
-       "      <td>4129250</td>\n",
-       "      <td>{44498945, 36534274, 36558851, 36560901, 36517...</td>\n",
-       "      <td>{36534274, 36558851, 36517896, 36556809, 36548...</td>\n",
-       "      <td>651</td>\n",
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-       "      <td>0.973094</td>\n",
-       "      <td>0.644554</td>\n",
-       "      <td>6</td>\n",
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-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>4181342</td>\n",
-       "      <td>{44498945, 36534274, 36558851, 36517896, 36556...</td>\n",
-       "      <td>{36534274, 36558851, 36517896, 36556809, 36548...</td>\n",
-       "      <td>651</td>\n",
-       "      <td>192</td>\n",
-       "      <td>0.973094</td>\n",
-       "      <td>0.772242</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>439746</td>\n",
-       "      <td>{44498945, 36534274, 36558851, 36517896, 36556...</td>\n",
-       "      <td>{36534274, 36558851, 36517896, 36556809, 36548...</td>\n",
-       "      <td>651</td>\n",
-       "      <td>158</td>\n",
-       "      <td>0.973094</td>\n",
-       "      <td>0.804697</td>\n",
-       "      <td>4</td>\n",
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-       "     concept_id                                        descendants  \\\n",
-       "249     4129247  {44498945, 36534274, 36558851, 36560901, 36517...   \n",
-       "212    44783159  {44498945, 36534274, 36558851, 36560901, 36517...   \n",
-       "154     4129250  {44498945, 36534274, 36558851, 36560901, 36517...   \n",
-       "99      4181342  {44498945, 36534274, 36558851, 36517896, 36556...   \n",
-       "4        439746  {44498945, 36534274, 36558851, 36517896, 36556...   \n",
-       "\n",
-       "                                                 found  coverage  pollution  \\\n",
-       "249  {36534274, 36558851, 36517896, 36556809, 36548...       651        378   \n",
-       "212  {36534274, 36558851, 36517896, 36556809, 36548...       651        366   \n",
-       "154  {36534274, 36558851, 36517896, 36556809, 36548...       651        359   \n",
-       "99   {36534274, 36558851, 36517896, 36556809, 36548...       651        192   \n",
-       "4    {36534274, 36558851, 36517896, 36556809, 36548...       651        158   \n",
-       "\n",
-       "     completeness    purity  max_depth  \n",
-       "249      0.973094  0.632653          8  \n",
-       "212      0.973094  0.640118          8  \n",
-       "154      0.973094  0.644554          6  \n",
-       "99       0.973094  0.772242          5  \n",
-       "4        0.973094  0.804697          4  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C14: Other and ill-defined sites in lip, oral cavity and pharynx (384 candidates)\n"
-     ]
-    },
-    {
-     "data": {
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-       "      <th>concept_id</th>\n",
-       "      <th>descendants</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>132</th>\n",
-       "      <td>4115106</td>\n",
-       "      <td>{42598582, 42598955, 42599092, 42599192, 43266...</td>\n",
-       "      <td>{36564997, 36526091, 36528139, 36556815, 36522...</td>\n",
-       "      <td>379</td>\n",
-       "      <td>22528</td>\n",
-       "      <td>0.986979</td>\n",
-       "      <td>0.016545</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>4111017</td>\n",
-       "      <td>{4194462, 42598582, 4194827, 132258, 42599689,...</td>\n",
-       "      <td>{36564997, 36526091, 36528139, 36556815, 36522...</td>\n",
-       "      <td>379</td>\n",
-       "      <td>22419</td>\n",
-       "      <td>0.986979</td>\n",
-       "      <td>0.016624</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>4028253</td>\n",
-       "      <td>{42598582, 42599844, 132565, 132573, 42599972,...</td>\n",
-       "      <td>{36564997, 36526091, 36528139, 36556815, 36522...</td>\n",
-       "      <td>379</td>\n",
-       "      <td>22101</td>\n",
-       "      <td>0.986979</td>\n",
-       "      <td>0.016859</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>196931</td>\n",
-       "      <td>{44498945, 36536321, 44498948, 36536324, 44498...</td>\n",
-       "      <td>{36564997, 36526091, 36528139, 36556815, 36522...</td>\n",
-       "      <td>379</td>\n",
-       "      <td>18788</td>\n",
-       "      <td>0.986979</td>\n",
-       "      <td>0.019774</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>128</th>\n",
-       "      <td>4184252</td>\n",
-       "      <td>{44498945, 4096016, 4194323, 36536341, 4449896...</td>\n",
-       "      <td>{36564997, 36526091, 36528139, 36556815, 36522...</td>\n",
-       "      <td>379</td>\n",
-       "      <td>6049</td>\n",
-       "      <td>0.986979</td>\n",
-       "      <td>0.058961</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
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-      "text/plain": [
-       "     concept_id                                        descendants  \\\n",
-       "132     4115106  {42598582, 42598955, 42599092, 42599192, 43266...   \n",
-       "17      4111017  {4194462, 42598582, 4194827, 132258, 42599689,...   \n",
-       "92      4028253  {42598582, 42599844, 132565, 132573, 42599972,...   \n",
-       "18       196931  {44498945, 36536321, 44498948, 36536324, 44498...   \n",
-       "128     4184252  {44498945, 4096016, 4194323, 36536341, 4449896...   \n",
-       "\n",
-       "                                                 found  coverage  pollution  \\\n",
-       "132  {36564997, 36526091, 36528139, 36556815, 36522...       379      22528   \n",
-       "17   {36564997, 36526091, 36528139, 36556815, 36522...       379      22419   \n",
-       "92   {36564997, 36526091, 36528139, 36556815, 36522...       379      22101   \n",
-       "18   {36564997, 36526091, 36528139, 36556815, 36522...       379      18788   \n",
-       "128  {36564997, 36526091, 36528139, 36556815, 36522...       379       6049   \n",
-       "\n",
-       "     completeness    purity  max_depth  \n",
-       "132      0.986979  0.016545          7  \n",
-       "17       0.986979  0.016624          7  \n",
-       "92       0.986979  0.016859          6  \n",
-       "18       0.986979  0.019774          5  \n",
-       "128      0.986979  0.058961          9  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "all_parents = {}\n",
-    "all_candidates = {}\n",
-    "\n",
-    "for topography in topog_concepts.itertuples():\n",
-    "    code = topography.concept_code\n",
-    "    seeds = set(hn_concepts[hn_concepts.concept_code.str.contains(code)&hn_concepts.invalid_reason.isna()].concept_id)\n",
-    "    print(f\"{code}: {topography.concept_name} ({len(seeds)} candidates)\")\n",
-    "    all_parents[code] = find_common_parents(seeds=seeds, kg=kg)\n",
-    "    all_candidates[code] = pd.DataFrame([{'concept_id': c} | asdict(v) for c, v in all_parents[code].items()])\n",
-    "    best_result = all_candidates[code].sort_values(['coverage', 'purity']).tail(5)\n",
-    "    display(best_result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2ff820af",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kg-core (3.12.10)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/04_mapping_support.ipynb b/notebooks/04_mapping_support.ipynb
deleted file mode 100644
index 1543f24..0000000
--- a/notebooks/04_mapping_support.ipynb
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.12.0"
-  }
- },
- "cells": []
-}
\ No newline at end of file
diff --git a/notebooks/99_figures.ipynb b/notebooks/99_figures.ipynb
deleted file mode 100644
index 85911f0..0000000
--- a/notebooks/99_figures.ipynb
+++ /dev/null
@@ -1,4930 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "12d9d4e5",
-   "metadata": {},
-   "source": [
-    "## Constraint-aware semantic resolution over OMOP vocabularies\n",
-    "\n",
-    "* Pydantic classes (via linkml) produce structured LLM queries\n",
-    "* the LLM proposes candidates\n",
-    "* LinkML defines valid target spaces\n",
-    "* the KG enforces ontological correctness\n",
-    "* scoring / ranking resolves ambiguity\n",
-    "\n",
-    "### Reasoners\n",
-    "\n",
-    "1. Hierarchy reasoner: Strict checks for candidate ⊑ allowed_parent\n",
-    "2. Domain reasoner: Broader checks simply confirming if this concept is in the correct OMOP domain?\n",
-    "3. Vocabulary preferences: List of ordered vocab targets \n",
-    "4. Standardness and validity\n",
-    "5. Specificity / depth: Out of concepts matching selected restrictions, which is at the most appropriate level of specificity?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "7a6fd293",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sqlalchemy.orm import sessionmaker\n",
-    "from dotenv import load_dotenv\n",
-    "\n",
-    "from omop_graph.graph.kg import KnowledgeGraph\n",
-    "from omop_graph.render import (\n",
-    "    bind_default_renderers,\n",
-    ")\n",
-    "from orm_loader.helpers import configure_logging\n",
-    "from omop_alchemy import get_engine_name\n",
-    "import sqlalchemy as sa"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ce23b2dc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2026-02-06 16:14:11,909 | INFO     | sql_loader.omop_alchemy.config | Default database engine configured\n"
-     ]
-    }
-   ],
-   "source": [
-    "configure_logging()\n",
-    "load_dotenv()\n",
-    "\n",
-    "engine_string = get_engine_name()\n",
-    "engine = sa.create_engine(engine_string, future=True, echo=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "fff93617",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Engine(postgresql+psycopg2://hemonc_user:***@localhost:5555/hemonc_db)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "engine"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "934b60ab",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Session = sessionmaker(bind=engine)\n",
-    "\n",
-    "session = Session()\n",
-    "kg = KnowledgeGraph(session)\n",
-    "bind_default_renderers(kg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "a7ebd037",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ConceptView(id=45921297, CIEL:143326, name='Coronary Artery Thrombosis')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kg.concept_view(45921297)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "ab83927f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(LabelMatch(input_label='heart attack', matched_label='Heart attack', concept_id=36210764, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=True, is_active=True),\n",
-       " LabelMatch(input_label='heart attack', matched_label='Heart attack', concept_id=45466858, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True))"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kg.label_lookup(\"Heart attack\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "4592f774",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=4134723, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=True, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=3309601, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary Artery Thrombosis', concept_id=45921297, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=3161942, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=False),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=40623902, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=False)]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(kg.label_lookup(\"Coronary artery thrombosis\", fuzzy=False))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "2d4a8b6f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[LabelMatch(input_label='coronary artery thrombosis', matched_label='Right main coronary artery thrombosis', concept_id=4304192, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=True, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=4134723, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=True, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Left main coronary artery thrombosis', concept_id=4209308, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=True, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Left anterior descending coronary artery thrombosis', concept_id=4153091, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=True, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Right Main Coronary Artery Thrombosis', concept_id=45908931, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary Artery Thrombosis', concept_id=45921297, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Left anterior descending coronary artery thrombosis', concept_id=3403562, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Left Main Coronary Artery Thrombosis', concept_id=45915508, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Left Anterior Descending Coronary Artery Thrombosis', concept_id=45955194, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Right main coronary artery thrombosis', concept_id=3169041, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=3309601, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Left main coronary artery thrombosis', concept_id=3270140, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=True),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=40623902, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=False),\n",
-       " LabelMatch(input_label='coronary artery thrombosis', matched_label='Coronary artery thrombosis', concept_id=3161942, match_kind=<LabelMatchKind.DIRECT: 1>, is_standard=False, is_active=False)]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(kg.label_lookup(\"Coronary artery thrombosis\", fuzzy=True))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "7c964c57",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from omop_graph.reasoning.term_grounding import GroundingConstraints, ground_term\n",
-    "from omop_graph.reasoning.resolvers import ResolverPipeline, ExactLabelResolver, PartialLabelResolver, ExactSynonymResolver\n",
-    "from omop_graph.reasoning.term_grounding import _passes_constraints\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "69726c8e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "constraints = GroundingConstraints(\n",
-    "    parent_ids=(37153816,),         # malignant neoplasm\n",
-    "    allowed_domains=(\"Condition\",), # redundant with parent_ids but perhaps we support combinations across domains eventually for more abstract grounding?\n",
-    "    allowed_vocabularies=(\"SNOMED\", \"ICDO3\"),\n",
-    "    require_standard=True,\n",
-    "    max_depth=4,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "ec17c091",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resolver_pipeline = ResolverPipeline(\n",
-    "    resolvers=(ExactLabelResolver(),ExactSynonymResolver(),PartialLabelResolver()),\n",
-    "    stop_after_confidence=PartialLabelResolver.confidence\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "b2400dda",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ground_term(kg, \"hodgkin lymphoma\", constraints=constraints, resolver_pipeline=resolver_pipeline)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "2bc5a61a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "e = ExactLabelResolver()\n",
-    "p = PartialLabelResolver()\n",
-    "s = ExactSynonymResolver()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "45a48125",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hits = e.resolve(kg, \"hodgkin lymphoma\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "62bd9fe8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[CandidateHit(concept_id=37151896, resolver='exact_label'),\n",
-       " CandidateHit(concept_id=36311303, resolver='exact_label'),\n",
-       " CandidateHit(concept_id=37093686, resolver='exact_label'),\n",
-       " CandidateHit(concept_id=42485178, resolver='exact_label'),\n",
-       " CandidateHit(concept_id=45463156, resolver='exact_label'),\n",
-       " CandidateHit(concept_id=1567633, resolver='exact_label'),\n",
-       " CandidateHit(concept_id=45576344, resolver='exact_label'),\n",
-       " CandidateHit(concept_id=37615263, resolver='exact_label'),\n",
-       " CandidateHit(concept_id=1406737, resolver='exact_label'),\n",
-       " CandidateHit(concept_id=4031667, resolver='exact_label')]"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "8b45b6eb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Concept ID 37151896 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 36311303 passes: False, reasons: [\"domain Meas Value not in ('Condition',)\"]\n",
-      "Concept ID 37093686 passes: False, reasons: ['vocabulary ICD10GM not allowed']\n",
-      "Concept ID 42485178 passes: False, reasons: ['vocabulary KCD7 not allowed']\n",
-      "Concept ID 45463156 passes: False, reasons: ['vocabulary Read not allowed']\n",
-      "Concept ID 1567633 passes: False, reasons: ['vocabulary ICD10CM not allowed']\n",
-      "Concept ID 45576344 passes: False, reasons: ['vocabulary ICD10 not allowed']\n",
-      "Concept ID 37615263 passes: False, reasons: ['vocabulary CIM10 not allowed']\n",
-      "Concept ID 1406737 passes: False, reasons: ['vocabulary ICD10CN not allowed']\n",
-      "Concept ID 4031667 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n"
-     ]
-    }
-   ],
-   "source": [
-    "for h in hits:\n",
-    "    ok, reasons = _passes_constraints(kg, h.concept_id, constraints)\n",
-    "    print(f\"Concept ID {h.concept_id} passes: {ok}, reasons: {reasons}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "1dd00c08",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hits = s.resolve(kg, \"hodgkin lymphoma\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "85322723",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for h in hits:\n",
-    "    ok, reasons = _passes_constraints(kg, h.concept_id, constraints)\n",
-    "    print(f\"Concept ID {h.concept_id} passes: {ok}, reasons: {reasons}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b4a7152a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 4329847 is the only concept that passes constraints and matches the selected grounding returned above"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "c2c96ab4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hits = p.resolve(kg, \"hodgkin lymphoma\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "b509ebfd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Concept ID 37093686 passes: False, reasons: ['vocabulary ICD10GM not allowed']\n",
-      "Concept ID 37151896 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 37615263 passes: False, reasons: ['vocabulary CIM10 not allowed']\n",
-      "Concept ID 45463156 passes: False, reasons: ['vocabulary Read not allowed']\n",
-      "Concept ID 4031667 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 36311303 passes: False, reasons: [\"domain Meas Value not in ('Condition',)\"]\n",
-      "Concept ID 1567633 passes: False, reasons: ['vocabulary ICD10CM not allowed']\n",
-      "Concept ID 45576344 passes: False, reasons: ['vocabulary ICD10 not allowed']\n",
-      "Concept ID 1406737 passes: False, reasons: ['vocabulary ICD10CN not allowed']\n",
-      "Concept ID 42485178 passes: False, reasons: ['vocabulary KCD7 not allowed']\n",
-      "Concept ID 1553022 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 45433102 passes: False, reasons: ['vocabulary Read not allowed']\n",
-      "Concept ID 1402163 passes: False, reasons: ['vocabulary ICD10CN not allowed']\n",
-      "Concept ID 777690 passes: False, reasons: ['vocabulary OncoTree not allowed']\n",
-      "Concept ID 44499281 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 4028861 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 37093693 passes: False, reasons: ['vocabulary ICD10GM not allowed']\n",
-      "Concept ID 37615571 passes: False, reasons: ['vocabulary CIM10 not allowed']\n",
-      "Concept ID 1567640 passes: False, reasons: ['vocabulary ICD10CM not allowed']\n",
-      "Concept ID 45581232 passes: False, reasons: ['vocabulary ICD10 not allowed']\n",
-      "Concept ID 1406752 passes: False, reasons: ['vocabulary ICD10CN not allowed']\n",
-      "Concept ID 1406753 passes: False, reasons: ['vocabulary ICD10CN not allowed']\n",
-      "Concept ID 42485185 passes: False, reasons: ['vocabulary KCD7 not allowed']\n",
-      "Concept ID 1402156 passes: False, reasons: ['vocabulary ICD10CN not allowed']\n",
-      "Concept ID 3430738 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 1397914 passes: False, reasons: ['vocabulary Read not allowed']\n",
-      "Concept ID 44499282 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 4028701 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 3452776 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 3454287 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 4179351 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 4216428 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 45571548 passes: False, reasons: ['vocabulary ICD10CM not allowed']\n",
-      "Concept ID 3443626 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 4012175 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 45936962 passes: False, reasons: ['vocabulary CIEL not allowed']\n",
-      "Concept ID 42542185 passes: False, reasons: ['vocabulary HemOnc not allowed']\n",
-      "Concept ID 36540835 passes: True, reasons: []\n",
-      "Concept ID 36539567 passes: True, reasons: []\n",
-      "Concept ID 36555113 passes: True, reasons: []\n",
-      "Concept ID 36544940 passes: True, reasons: []\n",
-      "Concept ID 36536539 passes: True, reasons: []\n",
-      "Concept ID 36402763 passes: False, reasons: ['concept is non-standard']\n",
-      "Concept ID 36525284 passes: True, reasons: []\n",
-      "Concept ID 44503243 passes: False, reasons: ['concept is non-standard']\n",
-      "Concept ID 36529144 passes: True, reasons: []\n",
-      "Concept ID 44505365 passes: True, reasons: []\n",
-      "Concept ID 36535471 passes: True, reasons: []\n",
-      "Concept ID 36538924 passes: True, reasons: []\n",
-      "Concept ID 36524874 passes: True, reasons: []\n",
-      "Concept ID 36554928 passes: True, reasons: []\n",
-      "Concept ID 36565324 passes: True, reasons: []\n",
-      "Concept ID 36539366 passes: True, reasons: []\n",
-      "Concept ID 36542532 passes: True, reasons: []\n",
-      "Concept ID 36563256 passes: True, reasons: []\n",
-      "Concept ID 36565037 passes: True, reasons: []\n",
-      "Concept ID 36544798 passes: True, reasons: []\n",
-      "Concept ID 36546669 passes: True, reasons: []\n",
-      "Concept ID 36563078 passes: True, reasons: []\n",
-      "Concept ID 36561341 passes: True, reasons: []\n",
-      "Concept ID 36538414 passes: True, reasons: []\n",
-      "Concept ID 36538420 passes: True, reasons: []\n",
-      "Concept ID 36538031 passes: True, reasons: []\n",
-      "Concept ID 36524234 passes: True, reasons: []\n",
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-      "Concept ID 45557019 passes: False, reasons: ['vocabulary ICD10CM not allowed']\n",
-      "Concept ID 45566621 passes: False, reasons: ['vocabulary ICD10CM not allowed']\n",
-      "Concept ID 36551016 passes: True, reasons: []\n",
-      "Concept ID 36541554 passes: True, reasons: []\n",
-      "Concept ID 36542361 passes: True, reasons: []\n",
-      "Concept ID 36542698 passes: True, reasons: []\n",
-      "Concept ID 36539697 passes: True, reasons: []\n",
-      "Concept ID 45557035 passes: False, reasons: ['vocabulary ICD10CM not allowed']\n",
-      "Concept ID 36536234 passes: True, reasons: []\n",
-      "Concept ID 45566644 passes: False, reasons: ['vocabulary ICD10CM not allowed']\n",
-      "Concept ID 37018877 passes: False, reasons: ['concept is non-standard']\n",
-      "Concept ID 37016150 passes: False, reasons: ['concept is non-standard']\n",
-      "Concept ID 36557706 passes: True, reasons: []\n",
-      "Concept ID 45590928 passes: False, reasons: ['vocabulary ICD10CM not allowed']\n",
-      "Concept ID 35909772 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 36543666 passes: True, reasons: []\n",
-      "Concept ID 36545627 passes: True, reasons: []\n",
-      "Concept ID 36517471 passes: True, reasons: []\n",
-      "Concept ID 778138 passes: False, reasons: ['vocabulary OncoTree not allowed']\n",
-      "Concept ID 1260365 passes: False, reasons: [\"domain Measurement not in ('Condition',)\"]\n",
-      "Concept ID 36540995 passes: True, reasons: []\n",
-      "Concept ID 36559089 passes: True, reasons: []\n",
-      "Concept ID 36541128 passes: True, reasons: []\n",
-      "Concept ID 45576363 passes: False, reasons: ['vocabulary ICD10CM not allowed']\n",
-      "Concept ID 36530412 passes: True, reasons: []\n",
-      "Concept ID 36532840 passes: True, reasons: []\n",
-      "Concept ID 36567903 passes: True, reasons: []\n",
-      "Concept ID 36544836 passes: True, reasons: []\n",
-      "Concept ID 36561904 passes: True, reasons: []\n",
-      "Concept ID 36544731 passes: True, reasons: []\n",
-      "Concept ID 36551969 passes: True, reasons: []\n",
-      "Concept ID 36567933 passes: True, reasons: []\n",
-      "Concept ID 36534857 passes: True, reasons: []\n",
-      "Concept ID 36533588 passes: True, reasons: []\n",
-      "Concept ID 36536028 passes: True, reasons: []\n",
-      "Concept ID 36532332 passes: True, reasons: []\n",
-      "Concept ID 36712837 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 3439711 passes: False, reasons: [\"domain Observation not in ('Condition',)\"]\n",
-      "Concept ID 1259499 passes: False, reasons: [\"domain Measurement not in ('Condition',)\"]\n",
-      "Concept ID 36529853 passes: True, reasons: []\n",
-      "Concept ID 36554387 passes: True, reasons: []\n",
-      "Concept ID 36558777 passes: True, reasons: []\n",
-      "Concept ID 36528719 passes: True, reasons: []\n",
-      "Concept ID 36525400 passes: True, reasons: []\n",
-      "Concept ID 36561493 passes: True, reasons: []\n",
-      "Concept ID 3387091 passes: False, reasons: ['vocabulary Nebraska Lexicon not allowed']\n",
-      "Concept ID 36716775 passes: True, reasons: []\n",
-      "Concept ID 36561608 passes: True, reasons: []\n",
-      "Concept ID 36567376 passes: True, reasons: []\n",
-      "Concept ID 36557294 passes: True, reasons: []\n",
-      "Concept ID 36554289 passes: True, reasons: []\n",
-      "Concept ID 36552339 passes: True, reasons: []\n",
-      "Concept ID 36530181 passes: True, reasons: []\n",
-      "Concept ID 36559182 passes: True, reasons: []\n"
-     ]
-    }
-   ],
-   "source": [
-    "for h in hits:\n",
-    "    ok, reasons = _passes_constraints(kg, h.concept_id, constraints)\n",
-    "    print(f\"Concept ID {h.concept_id} passes: {ok}, reasons: {reasons}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "c026d1ef",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[CandidateHit(concept_id=36540835, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539567, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555113, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544940, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536539, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525284, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529144, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44505365, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535471, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538924, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524874, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554928, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565324, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539366, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542532, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563256, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565037, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544798, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546669, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563078, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561341, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538414, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538420, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538031, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524234, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553925, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565906, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536971, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551165, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532848, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536897, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36567717, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44502493, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36518352, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527498, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36567190, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565589, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517582, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558535, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548127, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44505366, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540035, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532344, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537269, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535531, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527613, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538134, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524397, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527638, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548454, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36518976, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542492, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543379, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543347, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537245, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519189, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36531150, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552513, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565987, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527088, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540553, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523613, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559766, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529259, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526007, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565301, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546937, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528049, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539641, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36518048, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522741, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44502199, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36531135, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44499589, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559154, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540672, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546468, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524306, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36518889, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556423, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36531625, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550575, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549283, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546238, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549164, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549285, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563745, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554952, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44502590, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562539, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537854, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551388, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542254, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554486, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520979, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36567547, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545929, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525913, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552608, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523122, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549595, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547341, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548303, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36518882, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521026, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517697, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536069, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524399, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44501432, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538370, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565821, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544094, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561602, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526417, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562143, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554730, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562406, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530102, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539747, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565132, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554774, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544252, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556119, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561111, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536945, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551081, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36534154, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562032, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565356, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533038, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542922, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524543, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527529, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549301, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565762, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523804, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547847, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562942, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36531475, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563813, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528915, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536182, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542599, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550918, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559992, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555624, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537962, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544936, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549807, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520326, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544451, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533710, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541420, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559070, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528269, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551094, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556068, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36518129, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44501605, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529276, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561562, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540107, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560811, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543685, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532126, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533375, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36518157, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536388, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558104, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554896, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545362, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556137, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535461, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36518018, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550938, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528243, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548224, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538102, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527137, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561812, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36557817, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539796, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538359, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562138, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555783, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546267, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562195, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551089, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530754, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554488, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522871, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532446, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535512, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555549, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540190, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540546, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558156, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539006, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529306, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529354, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544991, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540422, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564215, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538931, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564805, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547329, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522919, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519997, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554171, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539860, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550651, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36557832, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533393, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524686, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522009, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550254, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524606, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552612, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552673, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555823, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36531461, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550982, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525343, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536937, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36534759, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544273, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524576, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539551, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563097, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542713, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553135, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528203, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542854, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547980, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528729, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564471, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546997, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517406, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540098, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559329, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523549, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564418, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563465, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553010, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561016, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541770, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539539, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541601, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548724, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560743, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543142, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=4038839, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550302, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548334, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556788, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44503303, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533424, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560035, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36566276, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548458, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36531213, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543607, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562882, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559870, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44499846, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526098, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36568016, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520677, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44500701, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563750, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550020, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520775, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519413, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550801, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524061, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517333, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560219, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553422, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559377, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517718, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532363, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36566181, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556417, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36531663, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525354, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520536, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539372, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559900, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552168, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563875, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527828, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44503258, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562200, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543318, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545927, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525771, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539413, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563402, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528895, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540505, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519934, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538455, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520649, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558015, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44503304, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539801, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561964, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546733, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542257, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542510, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548514, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519891, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564748, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528562, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553030, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563512, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564499, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532544, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44503277, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538610, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535445, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545232, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541252, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549104, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519570, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540537, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530632, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549683, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527881, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558733, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538142, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564389, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554838, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554328, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530242, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551920, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520549, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551886, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520758, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558628, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537534, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527691, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564140, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528246, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560133, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559970, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44505368, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550586, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537556, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562737, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546356, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36534693, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548466, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517855, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525845, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44503056, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552771, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539411, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526652, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536262, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562076, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544433, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551528, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530578, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559873, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564174, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520126, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551654, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552859, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520491, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36518641, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539359, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36557671, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517880, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527775, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36566772, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553958, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529623, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527694, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530297, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536032, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554277, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551427, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543416, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522599, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523832, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540858, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562621, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560493, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537988, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533844, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551962, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520031, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517564, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522111, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562634, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559386, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535187, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36531320, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541505, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532744, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529956, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520385, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559578, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535390, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36402814, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527223, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564705, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541807, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540965, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543274, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525223, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552881, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524875, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537696, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537811, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527299, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526973, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546399, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556465, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526325, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563907, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36566800, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553461, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565192, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537886, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523326, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539930, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546052, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537059, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543352, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547555, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36518457, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524724, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36531930, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532682, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540770, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536300, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552129, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520765, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521172, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545134, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554190, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523834, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527819, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548945, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549224, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517906, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539007, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522600, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537533, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558686, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564281, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564139, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559839, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549812, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522847, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542424, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536227, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553096, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533892, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529510, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36567835, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44503278, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553983, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533242, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548311, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551423, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544662, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528153, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526036, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553323, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555435, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564469, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521828, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546067, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543057, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553601, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526060, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546752, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546976, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553101, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542934, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533254, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522017, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523947, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532780, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538581, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543460, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520540, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36531872, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551410, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535698, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548056, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532229, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36402770, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540859, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535354, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537583, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532095, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549425, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544108, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36531897, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539205, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36531403, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517486, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550104, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559200, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561445, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562670, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36402750, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541550, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558437, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520029, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562943, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542493, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36567624, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555200, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36557141, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538937, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522301, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562951, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561825, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565413, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542448, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543887, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543904, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559104, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555293, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529361, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526879, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522977, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555941, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550912, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536274, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541962, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522054, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548812, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556458, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522950, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528443, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564456, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36567991, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560659, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521404, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560122, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545186, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522826, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549773, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536719, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528136, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36518588, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548447, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546146, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519203, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36531243, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529342, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537029, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539679, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546245, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526117, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529025, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525220, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546613, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533371, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555845, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517811, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546053, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550306, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556023, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521638, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541209, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549393, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558519, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549264, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560372, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533761, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562461, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562099, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552298, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517285, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522436, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526030, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524149, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523634, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564235, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545705, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36566430, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533736, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537786, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524192, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527026, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550157, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556221, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544495, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555802, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533850, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542112, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556923, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526645, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559771, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544123, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545766, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560972, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548704, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551487, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543124, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546485, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549580, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36534765, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545373, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559106, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44502592, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543684, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539276, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552643, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562319, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548017, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521025, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548981, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529861, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519652, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525499, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554740, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547168, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523951, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546271, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554525, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553540, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521807, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561947, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551245, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565264, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562301, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555247, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36518959, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565235, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525038, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556238, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547859, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551219, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550415, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519013, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563099, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547518, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536334, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559563, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540062, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36534878, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536420, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547397, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544224, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553519, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522120, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541291, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542102, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551612, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546015, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550242, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559862, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553484, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529438, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535302, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44500108, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553264, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44500279, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519493, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533119, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36567777, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563602, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559591, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545395, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548293, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563486, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554763, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548636, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520920, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564779, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541842, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533676, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521033, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564361, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529610, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36567301, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538813, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524694, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36518628, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520966, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552780, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540197, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547901, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553968, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561840, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551152, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547516, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555729, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533894, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535258, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553624, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530671, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564743, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548107, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554074, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530772, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545622, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535912, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517458, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526405, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563561, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522159, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550804, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541860, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563331, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44501297, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554330, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564322, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564811, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548079, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552543, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538372, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555127, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36534327, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541200, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555248, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545567, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537266, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562825, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544953, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556368, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517614, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561630, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541415, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524450, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36566253, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540570, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529524, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563068, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547831, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547653, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530997, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525778, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558637, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550928, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552311, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555366, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528873, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523986, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519568, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564254, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562597, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36534405, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556094, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552600, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563430, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521037, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523214, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521115, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555491, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536150, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536814, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554627, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545672, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563328, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538942, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543586, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528648, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542555, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539573, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544446, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36531294, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521000, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551782, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558034, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528042, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36518385, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36567064, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520447, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554253, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561018, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36557496, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36402824, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535710, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520850, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556219, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537407, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549121, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558422, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524790, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44499847, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521071, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523885, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523356, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547296, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558854, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524540, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521248, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530044, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552698, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553990, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550786, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550297, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540649, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532735, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548341, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543707, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538460, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541386, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556749, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538053, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533705, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542571, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528264, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36567796, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559237, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554788, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538172, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537357, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537860, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521007, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561902, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524379, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563145, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536136, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=44500533, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550059, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544843, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548987, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554187, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528893, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553879, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536346, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552679, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524881, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533412, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532188, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546583, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36546288, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36548131, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559419, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551555, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561986, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542400, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530897, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524872, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540655, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533691, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521724, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522586, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532873, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551865, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560409, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555915, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535291, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545676, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552031, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36557619, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564541, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552175, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526939, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559856, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540565, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559569, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530028, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530899, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519974, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530381, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551890, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558253, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539680, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535568, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36517803, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523911, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527102, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537754, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36542871, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526754, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541249, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523438, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545399, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564785, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565203, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527463, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564696, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526486, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522403, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560991, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537827, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529281, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524315, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545528, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36567189, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540620, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521326, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562861, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544266, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550724, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558790, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533395, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565941, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558308, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526285, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541941, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538318, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519753, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524102, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36558921, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539783, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560986, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550087, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522399, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36534512, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555291, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563055, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36566119, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537749, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537263, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529426, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551794, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36566866, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532157, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528902, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549996, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36562499, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565857, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535350, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36553704, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519504, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36556345, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563144, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36534426, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559872, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36520721, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36533455, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36561095, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543737, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539903, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560439, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555282, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524514, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554725, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36566018, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523079, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525272, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36564987, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535270, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36560276, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525125, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36530952, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552875, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36529583, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554004, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36523188, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551509, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543942, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36549303, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36536282, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36550497, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36541929, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539393, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519778, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36540517, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36566104, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526358, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36522875, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525384, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36528077, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36566696, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36524611, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563644, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552304, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526538, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36527879, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36566349, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36565873, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554548, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36547333, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36402847, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36539456, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36544043, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555583, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552520, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521470, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36551631, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36519576, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36566457, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36543335, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36525128, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36555430, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36563148, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36545204, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535097, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36552703, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36532872, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36526091, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36537467, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36554871, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36535076, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36521077, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36538506, resolver='partial_label'),\n",
-       " CandidateHit(concept_id=36559588, resolver='partial_label'),\n",
-       " ...]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[h for h in hits if _passes_constraints(kg, h.concept_id, constraints)[0]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c79ceccd",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kg-core (3.12.10)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pyproject.toml b/pyproject.toml
index 0ffce9a..c28d02f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -62,11 +62,11 @@ duckdb = [
 
 dev = [
     "ipython>=9.8.0",
-    "ty>=0.0.59",
+    "ty==0.0.90",
     "pytest>=9.0.2",
     "pytest-cov>=7.0.0",
     "rich>=14.2.0",
-    "ruff>=0.14.10",
+    "ruff==0.17.1",
     "omop-graph[duckdb,emb,faiss-cpu,pgvector,postgres]",
     "mkdocs<2.0",
     "mkdocs-material",
@@ -99,3 +99,10 @@ packages = ["src/omop_graph"]
 
 [tool.uv]
 cache-keys = [{ file = "pyproject.toml" }, { git = { commit = true, tags = true } }]
+
+[tool.ruff]
+line-length = 100
+
+[tool.ty.rules]
+# raises on stale suppressions
+unused-ignore-comment = "error"
diff --git a/scripts/benchmarks/benchmark.py b/scripts/benchmarks/benchmark.py
index 748a6d4..afcaf44 100644
--- a/scripts/benchmarks/benchmark.py
+++ b/scripts/benchmarks/benchmark.py
@@ -8,13 +8,17 @@
 from __future__ import annotations
 
 import json
+from collections.abc import Sequence
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Sequence, Tuple, cast, Annotated
-import typer
+from typing import Annotated, Any, cast
 
 import numpy as np
-from oa_configurator import Resolver, ResolvedModel, ResolvedProvider
+import typer
+from oa_configurator import ResolvedModel, ResolvedProvider, Resolver
+from omop_alchemy.cdm.query import ConceptFilter
+from omop_emb.backends import EmbeddingBackend, open_vector_store_writer
+from omop_emb.backends.index_config import index_config_from_index_type
 from omop_emb.config import (
     IndexType,
     MetricType,
@@ -22,12 +26,11 @@
     parse_metric_type,
 )
 from omop_emb.interface import EmbeddingRole
-from omop_emb.backends import EmbeddingBackend, open_vector_store_writer
-from omop_emb.backends.index_config import index_config_from_index_type
+
 from omop_graph.config import OmopGraphConfig
-from omop_graph.extensions.emb import get_embedding_writer_interface, MissingExtensionError
+from omop_graph.db.session import cdm_session_factory
+from omop_graph.extensions.emb import MissingExtensionError, get_embedding_writer_interface
 from omop_graph.extensions.omop_alchemy import PredicateKind
-from omop_alchemy.cdm.query import ConceptFilter
 from omop_graph.graph.kg import KnowledgeGraph, KnowledgeGraphEmbeddingConfiguration
 from omop_graph.graph.scoring import StandardConceptWithScore
 from omop_graph.reasoning.grounding import GroundingConstraints, ground_term
@@ -42,7 +45,7 @@
     PartialLabelResolver,
     PartialSynonymResolver,
 )
-from omop_graph.db.session import cdm_session_factory
+
 app = typer.Typer()
 
 
@@ -50,13 +53,18 @@
 def _main(
     verbose: Annotated[
         int,
-        typer.Option("--verbose", "-v", count=True, help="Increase log verbosity (-v INFO, -vv DEBUG). Must come before the subcommand name."),
+        typer.Option(
+            "--verbose",
+            "-v",
+            count=True,
+            help="Increase log verbosity (-v INFO, -vv DEBUG). Must come before the subcommand name.",
+        ),
     ] = 0,
 ) -> None:
     OmopGraphConfig.configure_logging(verbosity=verbose)
 
 
-def _normalize_parent_ids(raw_parent_ids: object) -> Optional[Tuple[int, ...]]:
+def _normalize_parent_ids(raw_parent_ids: object) -> tuple[int, ...] | None:
     """Normalize parent_ids from JSON into an optional tuple of ints."""
 
     if raw_parent_ids is None:
@@ -65,9 +73,7 @@ def _normalize_parent_ids(raw_parent_ids: object) -> Optional[Tuple[int, ...]]:
         return (raw_parent_ids,)
     if isinstance(raw_parent_ids, (list, tuple)):
         return tuple(int(parent_id) for parent_id in raw_parent_ids)
-    raise TypeError(
-        "Invalid parent_ids value. Expected int, list[int], tuple[int, ...], or null."
-    )
+    raise TypeError("Invalid parent_ids value. Expected int, list[int], tuple[int, ...], or null.")
 
 
 @dataclass(frozen=True)
@@ -97,11 +103,11 @@ class BenchmarkCase:
     id: str
     text: str
     bucket: str
-    domain: Optional[str] = None
-    vocabularies: Optional[Tuple[str, ...]] = None
-    expected_concept_id: Optional[int] = None
-    expected_concept_name: Optional[str] = None
-    parent_ids: Optional[Tuple[int, ...]] = None
+    domain: str | None = None
+    vocabularies: tuple[str, ...] | None = None
+    expected_concept_id: int | None = None
+    expected_concept_name: str | None = None
+    parent_ids: tuple[int, ...] | None = None
 
 
 @dataclass(frozen=True)
@@ -109,17 +115,17 @@ class GroundedBenchmarkConfig:
     """One ablation configuration for grounded benchmark evaluation."""
 
     name: str
-    resolvers: Tuple[CandidateResolver, ...]
+    resolvers: tuple[CandidateResolver, ...]
     requires_embedding: bool = False
 
 
-def load_cases(path: Path) -> List[BenchmarkCase]:
+def load_cases(path: Path) -> list[BenchmarkCase]:
     """Load benchmark cases from JSON into typed dataclass instances."""
 
     payload = json.loads(path.read_text(encoding="utf-8"))
 
     if isinstance(payload, list):
-        cases: List[BenchmarkCase] = []
+        cases: list[BenchmarkCase] = []
         for row in payload:
             if "parent_ids" in row:
                 row = {**row, "parent_ids": _normalize_parent_ids(row["parent_ids"])}
@@ -138,7 +144,7 @@ def load_cases(path: Path) -> List[BenchmarkCase]:
         return cases
 
     if isinstance(payload, dict):
-        cases: List[BenchmarkCase] = []
+        cases: list[BenchmarkCase] = []
         for bucket, bucket_cases in payload.items():
             for row in bucket_cases:
                 if "parent_ids" in row:
@@ -175,13 +181,10 @@ def build_embedding_knowledge_graph(
         write=True,
         compute_missing_embeddings=True,
     )
-    return KnowledgeGraph(
-        cdm_sessions,
-        emb_config=config
-    )
+    return KnowledgeGraph(cdm_sessions, emb_config=config)
 
 
-def case_constraints(case: BenchmarkCase) -> Optional[ConceptFilter]:
+def case_constraints(case: BenchmarkCase) -> ConceptFilter | None:
     """Translate case metadata into OMOP search constraints when available."""
 
     domains = (case.domain,) if case.domain else None
@@ -194,7 +197,7 @@ def case_constraints(case: BenchmarkCase) -> Optional[ConceptFilter]:
     )
 
 
-def percentile(values: List[float], p: int) -> float:
+def percentile(values: list[float], p: int) -> float:
     """Return a simple nearest-rank percentile for numeric values."""
 
     if not values:
@@ -205,11 +208,11 @@ def percentile(values: List[float], p: int) -> float:
 
 
 def mcnemar(
-    a: Sequence[Dict[str, float | int | bool | str]],
-    b: Sequence[Dict[str, float | int | bool | str]],
+    a: Sequence[dict[str, float | int | bool | str]],
+    b: Sequence[dict[str, float | int | bool | str]],
     *,
     field: str = "top1_correct",
-) -> Dict[str, float]:
+) -> dict[str, float]:
     """Compute continuity-corrected McNemar-style paired comparison."""
 
     paired = [(float(x[field]), float(y[field])) for x, y in zip(a, b, strict=True)]
@@ -226,9 +229,9 @@ def mcnemar(
 
 def ranking_metrics(
     predictions: Sequence[int],
-    expected: Optional[int],
+    expected: int | None,
     k: int,
-) -> Dict[str, float]:
+) -> dict[str, float]:
     """Compute top1/MRR/Recall@K for one ranked prediction list."""
 
     if expected is None:
@@ -245,7 +248,7 @@ def ranking_metrics(
     }
 
 
-def build_grounded_configs() -> Tuple[GroundedBenchmarkConfig, ...]:
+def build_grounded_configs() -> tuple[GroundedBenchmarkConfig, ...]:
     """Build resolver ablations for grounded benchmarking."""
 
     basic = (
@@ -281,8 +284,8 @@ def build_grounded_configs() -> Tuple[GroundedBenchmarkConfig, ...]:
 
 def _resolve_parent_ids(
     case: BenchmarkCase,
-    default_parent_ids: Optional[Tuple[int, ...]],
-) -> Optional[Tuple[int, ...]]:
+    default_parent_ids: tuple[int, ...] | None,
+) -> tuple[int, ...] | None:
     """Resolve grounding parent IDs from case-level or CLI-level defaults."""
 
     if case.parent_ids:
@@ -299,13 +302,13 @@ def _bucket_sort_key(bucket: str) -> tuple[int, str]:
     return (1, normalised)
 
 
-def _order_cases_for_report(cases: Sequence[BenchmarkCase]) -> List[BenchmarkCase]:
+def _order_cases_for_report(cases: Sequence[BenchmarkCase]) -> list[BenchmarkCase]:
     """Order cases so the easy bucket is shown first."""
 
     return sorted(cases, key=lambda case: (_bucket_sort_key(case.bucket), case.id))
 
 
-def _summarise_config(rows: Sequence[Dict[str, Any]], label: str) -> Dict[str, Any]:
+def _summarise_config(rows: Sequence[dict[str, Any]], label: str) -> dict[str, Any]:
     """Aggregate case-level ranking metrics into one configuration summary."""
 
     if not rows:
@@ -321,9 +324,9 @@ def _summarise_config(rows: Sequence[Dict[str, Any]], label: str) -> Dict[str, A
 
 
 def _print_summary_report(
-    summaries: Dict[str, Dict[str, Any]],
-    bucket_summaries: Dict[str, Dict[str, Dict[str, Any]]],
-    significance: Dict[str, Dict[str, float]],
+    summaries: dict[str, dict[str, Any]],
+    bucket_summaries: dict[str, dict[str, dict[str, Any]]],
+    significance: dict[str, dict[str, float]],
     k: int,
 ) -> None:
     """Print a formatted benchmark summary table to stdout."""
@@ -333,7 +336,7 @@ def _print_summary_report(
     header = f"{'Config':<{col_w[0]}} {'Top-1':>{col_w[1]}} {'MRR':>{col_w[2]}} {rk_label:>{col_w[3]}} {'N':>{col_w[4]}}"
     sep = "-" * len(header)
 
-    def _row(name: str, s: Dict[str, Any]) -> str:
+    def _row(name: str, s: dict[str, Any]) -> str:
         return (
             f"{name:<{col_w[0]}} "
             f"{float(s.get('top1_accuracy', 0.0)):>{col_w[1]}.3f} "
@@ -381,14 +384,16 @@ def _row(name: str, s: Dict[str, Any]) -> str:
 
 def _grounded_element_to_dict(
     concept: StandardConceptWithScore,
-) -> Dict[str, object]:
-    
+) -> dict[str, object]:
+
     return {
         "concept_id": int(concept.concept_id),
         "concept_name": concept.concept_name,
         "total_score": float(concept.total_score),
         "relevance": float(concept.relevance),
-        "embedding_score": float(concept.embedding_score) if concept.embedding_score is not None else 0.0,
+        "embedding_score": float(concept.embedding_score)
+        if concept.embedding_score is not None
+        else 0.0,
         "separation": int(concept.separation),
         "matched_concept_label": concept.matched_concept_label,
         "match_kind": str(concept.match_kind),
@@ -396,12 +401,12 @@ def _grounded_element_to_dict(
     }
 
 
-def _actual_payload(grounded: Sequence[StandardConceptWithScore]) -> Dict[str, object]:
+def _actual_payload(grounded: Sequence[StandardConceptWithScore]) -> dict[str, object]:
     """Serialize the actual top grounded result for one config."""
     actual_concept = grounded[0] if grounded else None
-    #return {
+    # return {
     #    "actual": _grounded_element_to_dict(actual_concept) if actual_concept else None
-    #}
+    # }
     return {
         "actual": {
             "concept_id": int(actual_concept.concept_id) if actual_concept else None,
@@ -415,9 +420,9 @@ def _evaluate_grounded_case(
     kg: KnowledgeGraph,
     case: BenchmarkCase,
     config: GroundedBenchmarkConfig,
-    default_parent_ids: Optional[Tuple[int, ...]],
-    grounding_kwargs: Optional[Dict[str, object]] = None,
-) -> Dict[str, object]:
+    default_parent_ids: tuple[int, ...] | None,
+    grounding_kwargs: dict[str, object] | None = None,
+) -> dict[str, object]:
     """Evaluate one case using ``ground_term`` and return poster-friendly results."""
 
     search_constraint = case_constraints(case)
@@ -430,7 +435,7 @@ def _evaluate_grounded_case(
     resolver_pipeline = ResolverPipeline(resolvers=config.resolvers)
     grounding_kwargs = grounding_kwargs or {}
 
-    text_embedding = cast(Optional[np.ndarray], grounding_kwargs.get("text_embedding"))
+    text_embedding = cast(np.ndarray | None, grounding_kwargs.get("text_embedding"))
 
     grounded = ground_term(
         resolver_pipeline=resolver_pipeline,
@@ -455,7 +460,11 @@ def _evaluate_grounded_case(
         "target_concept_name": case.expected_concept_name,
         **_actual_payload(grounded),
         "target_idx_in_grounded": next(
-            (i for i, concept in enumerate(grounded) if concept.concept_id == case.expected_concept_id),
+            (
+                i
+                for i, concept in enumerate(grounded)
+                if concept.concept_id == case.expected_concept_id
+            ),
             None,
         ),
         "grounded": [_grounded_element_to_dict(concept) for concept in grounded],
@@ -464,59 +473,98 @@ def _evaluate_grounded_case(
 
 @app.command()
 def run_benchmark(
-    cases_file: Annotated[str, typer.Option(
-        "--cases-file", "-c", 
-        help="Path to the JSON file containing benchmark cases.")
+    cases_file: Annotated[
+        str,
+        typer.Option(
+            "--cases-file", "-c", help="Path to the JSON file containing benchmark cases."
+        ),
     ],
-    embedding_model: Annotated[Optional[str], typer.Option(
-        "--embedding-model", "-m",
-        help="Name of the embedding model to use (e.g., 'text-embedding-3-small'). Falls back to config.toml")
+    embedding_model: Annotated[
+        str | None,
+        typer.Option(
+            "--embedding-model",
+            "-m",
+            help="Name of the embedding model to use (e.g., 'text-embedding-3-small'). Falls back to config.toml",
+        ),
     ] = None,
-    embedding_api_base_url: Annotated[Optional[str], typer.Option(
-        "--embedding-api-base-url", "-u",
-        help="Base URL for the embedding API (e.g., 'http://localhost:8000'). Falls back to config.toml.")
+    embedding_api_base_url: Annotated[
+        str | None,
+        typer.Option(
+            "--embedding-api-base-url",
+            "-u",
+            help="Base URL for the embedding API (e.g., 'http://localhost:8000'). Falls back to config.toml.",
+        ),
     ] = None,
-    embedding_api_key: Annotated[Optional[str], typer.Option(
-        "--embedding-api-key", "-k",
-        help="API key for the embedding service, if required. Falls back to config.toml.")
+    embedding_api_key: Annotated[
+        str | None,
+        typer.Option(
+            "--embedding-api-key",
+            "-k",
+            help="API key for the embedding service, if required. Falls back to config.toml.",
+        ),
     ] = None,
-    embedding_metric_type: Annotated[MetricType, typer.Option(
-        "--embedding-metric-type", "-M",
-        help="Distance metric type for embedding similarity.")
+    embedding_metric_type: Annotated[
+        MetricType,
+        typer.Option(
+            "--embedding-metric-type", "-M", help="Distance metric type for embedding similarity."
+        ),
     ] = MetricType.COSINE,
-    embedding_index_type: Annotated[IndexType, typer.Option(
-        "--embedding-index-type", "-I",
-        help="Index type for embedding retrieval (e.g., 'flat'). Has to match the registered model.")
+    embedding_index_type: Annotated[
+        IndexType,
+        typer.Option(
+            "--embedding-index-type",
+            "-I",
+            help="Index type for embedding retrieval (e.g., 'flat'). Has to match the registered model.",
+        ),
     ] = IndexType.FLAT,
-    out_file: Annotated[Optional[str], typer.Option(
-        "--out-file", "-o",
-        help="Path to the output JSON file where results will be saved. If not provided, results will be printed to stdout.")
+    out_file: Annotated[
+        str | None,
+        typer.Option(
+            "--out-file",
+            "-o",
+            help="Path to the output JSON file where results will be saved. If not provided, results will be printed to stdout.",
+        ),
     ] = None,
-    k: Annotated[int, typer.Option(
-        "--k", "-K",
-        help="Number of nearest neighbors to retrieve for each case.")
+    k: Annotated[
+        int,
+        typer.Option("--k", "-K", help="Number of nearest neighbors to retrieve for each case."),
     ] = 5,
-    domains: Annotated[Optional[List[str]], typer.Option(
-        "--allowed-domains", "-D",
-        help="Used to filter cases within the case file. For multiple domains, repeat the option (e.g., -D Condition -D Procedure).")
+    domains: Annotated[
+        list[str] | None,
+        typer.Option(
+            "--allowed-domains",
+            "-D",
+            help="Used to filter cases within the case file. For multiple domains, repeat the option (e.g., -D Condition -D Procedure).",
+        ),
     ] = None,
-    allowed_vocabularies: Annotated[Optional[List[str]], typer.Option(
-        "--allowed-vocabularies", "-V",
-        help="Used to filter cases within the case file. For multiple vocabularies, repeat the option (e.g., -V SNOMED -V ICDO3).")
+    allowed_vocabularies: Annotated[
+        list[str] | None,
+        typer.Option(
+            "--allowed-vocabularies",
+            "-V",
+            help="Used to filter cases within the case file. For multiple vocabularies, repeat the option (e.g., -V SNOMED -V ICDO3).",
+        ),
     ] = None,
-    parent_ids: Annotated[Optional[List[str]], typer.Option(
-        "--grounding-parent-ids", "-G",
-        help="Overwrites the parent_ids specified in individual cases. For multiple IDs, repeat the option (e.g., -G 443392 -G 413015).")
+    parent_ids: Annotated[
+        list[str] | None,
+        typer.Option(
+            "--grounding-parent-ids",
+            "-G",
+            help="Overwrites the parent_ids specified in individual cases. For multiple IDs, repeat the option (e.g., -G 443392 -G 413015).",
+        ),
     ] = None,
-    vector_store_name: Annotated[Optional[str], typer.Option(
-        "--vector-store", "-e",
-        help="Name of a [vector_stores.*] entry to use for embedding storage. Defaults to the value configured via omop-config.")
+    vector_store_name: Annotated[
+        str | None,
+        typer.Option(
+            "--vector-store",
+            "-e",
+            help="Name of a [vector_stores.*] entry to use for embedding storage. Defaults to the value configured via omop-config.",
+        ),
     ] = None,
 ):
     """Generalised benchmark interface."""
     cases = load_cases(Path(cases_file))
 
-    
     if domains:
         domain_filter = set(domains)
         cases = [c for c in cases if c.domain in domain_filter]
@@ -533,15 +581,13 @@ def run_benchmark(
     else:
         grounding_parent_ids = None
     if grounding_parent_ids is None and all(c.parent_ids is None for c in cases):
-        raise RuntimeError(
-            "No grounding parent IDs provided."
-        )
+        raise RuntimeError("No grounding parent IDs provided.")
 
     embedding_kg = None
-    query_embeddings: Dict[str, np.ndarray] = {}
-    resolved_embedding_index_type: Optional[IndexType] = None
-    resolved_embedding_metric_type: Optional[MetricType] = None
-    canonical_model: Optional[str] = None
+    query_embeddings: dict[str, np.ndarray] = {}
+    resolved_embedding_index_type: IndexType | None = None
+    resolved_embedding_metric_type: MetricType | None = None
+    canonical_model: str | None = None
 
     if embedding_model is not None and embedding_api_base_url is not None:
         resolved_embedding_index_type = parse_index_type(embedding_index_type)
@@ -552,8 +598,10 @@ def run_benchmark(
         resolved_model = ResolvedModel(
             name="benchmark-embedding-model",
             provider=ResolvedProvider(
-                name="benchmark-embedding-provider", provider="ollama",
-                base_url=embedding_api_base_url, api_key=embedding_api_key,
+                name="benchmark-embedding-provider",
+                provider="ollama",
+                base_url=embedding_api_base_url,
+                api_key=embedding_api_key,
             ),
             model=embedding_model,
             embedding_dim=None,
@@ -583,7 +631,9 @@ def run_benchmark(
         )
 
         embedding_writer = get_embedding_writer_interface(embedding_kg)
-        assert embedding_writer is not None, "Embedding backend does not support writing embeddings, which is required for this benchmark configuration."
+        assert embedding_writer is not None, (
+            "Embedding backend does not support writing embeddings, which is required for this benchmark configuration."
+        )
 
         canonical_model = embedding_writer.model_backend.model
 
@@ -601,20 +651,20 @@ def run_benchmark(
     kg = KnowledgeGraph(cdm_session_factory())
     configs = build_grounded_configs()
 
-    errors: Dict[str, str] = {}
-    case_reports: List[Dict[str, Any]] = []
+    errors: dict[str, str] = {}
+    case_reports: list[dict[str, Any]] = []
     active_kg = embedding_kg if embedding_kg is not None else kg
 
     for case in cases:
-        config_results: List[Dict[str, Any]] = []
+        config_results: list[dict[str, Any]] = []
         for config in configs:
-            #try:
+            # try:
             if config.requires_embedding and embedding_kg is None:
                 raise MissingExtensionError(
                     "Embedding config requires omop-emb plus embedding model/api settings."
                 )
 
-            grounding_kwargs: Optional[Dict[str, object]] = None
+            grounding_kwargs: dict[str, object] | None = None
             if embedding_kg is not None and canonical_model is not None:
                 grounding_kwargs = {
                     "text_embedding": query_embeddings.get(case.id),
@@ -629,7 +679,7 @@ def run_benchmark(
             )
             config_results.append(row)
 
-            #except Exception as exc:
+            # except Exception as exc:
             #    errors[f"{case.id}:{config.name}"] = str(exc)
             #    config_results.append(
             #        {
@@ -652,7 +702,7 @@ def run_benchmark(
         )
 
     # Build per-config ranking rows for summary statistics.
-    per_config: Dict[str, List[Dict[str, Any]]] = {}
+    per_config: dict[str, list[dict[str, Any]]] = {}
     for case_report in case_reports:
         for cfg_result in case_report["config_results"]:
             config_name = str(cfg_result.get("config", ""))
@@ -666,32 +716,35 @@ def run_benchmark(
                 top1 = 1.0 if target_idx == 0 else 0.0
                 mrr_val = 1.0 / (int(target_idx) + 1)
                 rak = 1.0 if int(target_idx) < k else 0.0
-            per_config.setdefault(config_name, []).append({
-                "case_id": case_report["case_id"],
-                "bucket": case_report["bucket"],
-                "top1_correct": top1,
-                "mrr": mrr_val,
-                "recall_at_k": rak,
-            })
+            per_config.setdefault(config_name, []).append(
+                {
+                    "case_id": case_report["case_id"],
+                    "bucket": case_report["bucket"],
+                    "top1_correct": top1,
+                    "mrr": mrr_val,
+                    "recall_at_k": rak,
+                }
+            )
 
     summaries = {name: _summarise_config(rows, name) for name, rows in per_config.items()}
 
-    bucket_summaries: Dict[str, Dict[str, Dict[str, Any]]] = {}
+    bucket_summaries: dict[str, dict[str, dict[str, Any]]] = {}
     for config_name, rows in per_config.items():
-        by_bucket: Dict[str, List[Dict[str, Any]]] = {}
+        by_bucket: dict[str, list[dict[str, Any]]] = {}
         for row in rows:
             b = str(row["bucket"])
             by_bucket.setdefault(b, []).append(row)
         bucket_summaries[config_name] = {
-            b: _summarise_config(b_rows, f"{config_name}:{b}")
-            for b, b_rows in by_bucket.items()
+            b: _summarise_config(b_rows, f"{config_name}:{b}") for b, b_rows in by_bucket.items()
         }
 
-    significance: Dict[str, Dict[str, float]] = {}
+    significance: dict[str, dict[str, float]] = {}
     if "basic" in per_config and "extended" in per_config:
         significance["basic_vs_extended"] = mcnemar(per_config["basic"], per_config["extended"])
     if "extended" in per_config and "full_text" in per_config:
-        significance["extended_vs_full_text"] = mcnemar(per_config["extended"], per_config["full_text"])
+        significance["extended_vs_full_text"] = mcnemar(
+            per_config["extended"], per_config["full_text"]
+        )
     if "full_text" in per_config and "full_text_with_embedding" in per_config:
         significance["full_text_vs_full_text_with_embedding"] = mcnemar(
             per_config["full_text"], per_config["full_text_with_embedding"]
diff --git a/scripts/benchmarks/enrich_gold_standard.py b/scripts/benchmarks/enrich_gold_standard.py
index c418cc8..d391a74 100644
--- a/scripts/benchmarks/enrich_gold_standard.py
+++ b/scripts/benchmarks/enrich_gold_standard.py
@@ -22,9 +22,9 @@
 import argparse
 import json
 from collections import defaultdict
-from pathlib import Path
-from typing import Dict, List
 from dataclasses import dataclass
+from pathlib import Path
+from typing import Self
 
 import sqlalchemy as sa
 from omop_alchemy.cdm.model.vocabulary import Concept, Concept_Ancestor
@@ -32,12 +32,12 @@
 from omop_graph.db.session import cdm_session_factory
 
 
-def _domain_query(ids: List[int]) -> sa.Select:
+def _domain_query(ids: list[int]) -> sa.Select:
     """Each target concept's domain."""
     return sa.select(Concept.concept_id, Concept.domain_id).where(Concept.concept_id.in_(ids))
 
 
-def _ancestor_query(ids: List[int]) -> sa.Select:
+def _ancestor_query(ids: list[int]) -> sa.Select:
     """Standard strict ancestors of each target, with their name and domain."""
     return (
         sa.select(
@@ -55,6 +55,7 @@ def _ancestor_query(ids: List[int]) -> sa.Select:
         )
     )
 
+
 @dataclass
 class LevelStats:
     empty: int = 0
@@ -62,23 +63,24 @@ class LevelStats:
     single: int = 0
     multiple: int = 0
 
-    def __iadd__(self, other: "LevelStats") -> "LevelStats":
+    def __iadd__(self, other: LevelStats) -> Self:
         self.empty += other.empty
         self.cross_domain_fallback += other.cross_domain_fallback
         self.single += other.single
         self.multiple += other.multiple
         return self
 
+
 @dataclass
 class Stats:
-    levels: Dict[int, LevelStats]
+    levels: dict[int, LevelStats]
     no_target: int = 0
 
     @classmethod
-    def empty_for(cls, levels: List[int]) -> "Stats":
+    def empty_for(cls, levels: list[int]) -> Stats:
         return cls(levels={level: LevelStats() for level in levels})
 
-    def __iadd__(self, other: "Stats") -> "Stats":
+    def __iadd__(self, other: Stats) -> Self:
         for level, level_stats in other.levels.items():
             self.levels.setdefault(level, LevelStats())
             self.levels[level] += level_stats
@@ -86,7 +88,7 @@ def __iadd__(self, other: "Stats") -> "Stats":
         return self
 
 
-def enrich_cases(cdm_sessions, cases: List[Dict], levels: List[int]) -> Stats:
+def enrich_cases(cdm_sessions, cases: list[dict], levels: list[int]) -> Stats:
     target_ids = sorted({c["expected_concept_id"] for c in cases if c.get("expected_concept_id")})
 
     with cdm_sessions() as session:
@@ -95,7 +97,7 @@ def enrich_cases(cdm_sessions, cases: List[Dict], levels: List[int]) -> Stats:
             for row in session.execute(_domain_query(target_ids)).all()
         }
 
-        ancestors_by_target: Dict[int, List] = defaultdict(list)
+        ancestors_by_target: dict[int, list] = defaultdict(list)
         for row in session.execute(_ancestor_query(target_ids)).all():
             ancestors_by_target[row.descendant_concept_id].append(row)
 
@@ -111,7 +113,7 @@ def enrich_cases(cdm_sessions, cases: List[Dict], levels: List[int]) -> Stats:
         case_["domain"] = target_domain
         all_ancestors = ancestors_by_target.get(target_id, [])
 
-        parent_ids_by_level: Dict[str, List[int]] = {}
+        parent_ids_by_level: dict[str, list[int]] = {}
         for level in levels:
             at_level = [a for a in all_ancestors if a.min_levels_of_separation == level]
             same_domain = [a for a in at_level if a.domain_id == target_domain]
diff --git a/scripts/benchmarks/trace_example.py b/scripts/benchmarks/trace_example.py
index 33a7da7..e0f5847 100644
--- a/scripts/benchmarks/trace_example.py
+++ b/scripts/benchmarks/trace_example.py
@@ -18,9 +18,10 @@
 import logging
 from html import escape
 from pathlib import Path
-from typing import Annotated, Dict, List, Optional, Tuple
+from typing import Annotated
 
 import typer
+from omop_alchemy.cdm.query import ConceptFilter
 from rich.console import Console
 from rich.progress import (
     BarColumn,
@@ -30,12 +31,12 @@
     TextColumn,
     TimeElapsedColumn,
 )
+from sqlalchemy.exc import NoResultFound
 
 from omop_graph.config import OmopGraphConfig
 from omop_graph.db.session import cdm_session_factory
 from omop_graph.extensions.emb import get_embedding_writer_interface
 from omop_graph.extensions.omop_alchemy import PredicateKind
-from omop_alchemy.cdm.query import ConceptFilter
 from omop_graph.graph.kg import KnowledgeGraph, KnowledgeGraphEmbeddingConfiguration
 from omop_graph.graph.nodes import LabelMatchKind
 from omop_graph.reasoning.grounding import GroundingConstraints, ground_term
@@ -62,11 +63,17 @@
 def _main(
     verbose: Annotated[
         int,
-        typer.Option("--verbose", "-v", count=True, help="Increase log verbosity (-v INFO, -vv DEBUG). Must come before the subcommand name."),
+        typer.Option(
+            "--verbose",
+            "-v",
+            count=True,
+            help="Increase log verbosity (-v INFO, -vv DEBUG). Must come before the subcommand name.",
+        ),
     ] = 0,
 ) -> None:
     OmopGraphConfig.configure_logging(verbosity=verbose, console=console)
 
+
 # Display name for each resolver (inferred from match_kind + synonym flag)
 RESOLVER_DISPLAY_NAMES = {
     (LabelMatchKind.EXACT, False): "Exact",
@@ -89,9 +96,9 @@ def _main(
 ]
 
 # Groups synonym + non-synonym resolvers for the SVG column view
-RESOLVER_GROUPS: Tuple[Tuple[str, Tuple[str, ...]], ...] = (
-    ("Exact",     ("Exact", "Exact Synonym")),
-    ("Partial",   ("Partial", "Partial Synonym")),
+RESOLVER_GROUPS: tuple[tuple[str, tuple[str, ...]], ...] = (
+    ("Exact", ("Exact", "Exact Synonym")),
+    ("Partial", ("Partial", "Partial Synonym")),
     ("Full Text", ("Full Text", "Full Text Synonym")),
     ("Embedding", ("Embedding",)),
 )
@@ -99,7 +106,8 @@ def _main(
 
 # ─── Helpers ──────────────────────────────────────────────────────────────────
 
-def _build_kg(metric_type=None, embedding_model: Optional[str] = None) -> KnowledgeGraph:
+
+def _build_kg(metric_type=None, embedding_model: str | None = None) -> KnowledgeGraph:
     """Build a KG with embedding support, resolved from OmopGraphConfig."""
     cdm_sessions = cdm_session_factory()
     try:
@@ -126,18 +134,28 @@ def _build_kg(metric_type=None, embedding_model: Optional[str] = None) -> Knowle
             resolved_model=resolved_model,
             write=True,
         )
-        logger.info("Embedding config loaded (model=%s, metric=%s).", resolved_model.model, resolved_metric.value)
+        logger.info(
+            "Embedding config loaded (model=%s, metric=%s).",
+            resolved_model.model,
+            resolved_metric.value,
+        )
         return KnowledgeGraph(cdm_sessions, emb_config=emb_config)
     except Exception as exc:
-        logger.warning("Error occurred while loading embedding config:\n%s.\nRunning without embedding.", exc)
+        logger.warning(
+            "Error occurred while loading embedding config:\n%s.\nRunning without embedding.",
+            exc,
+            exc_info=True,
+        )
         return KnowledgeGraph(cdm_sessions)
 
 
 def _resolver_label(resolver) -> str:
-    return RESOLVER_DISPLAY_NAMES.get((resolver.match_kind, resolver.synonym), type(resolver).__name__)
+    return RESOLVER_DISPLAY_NAMES.get(
+        (resolver.match_kind, resolver.synonym), type(resolver).__name__
+    )
 
 
-def _resolve_embedding_model(embedding_model: Optional[str]) -> str:
+def _resolve_embedding_model(embedding_model: str | None) -> str:
     """Resolve the canonical embedding model name (override or config default).
 
     Mirrors the canonicalisation `_build_kg` does, but without opening a DB
@@ -158,7 +176,7 @@ def _resolve_embedding_model(embedding_model: Optional[str]) -> str:
     return build_model_backend_from_resolved(resolved_model).model
 
 
-def _model_dir_name(embedding_model: Optional[str]) -> str:
+def _model_dir_name(embedding_model: str | None) -> str:
     """Filesystem-safe directory name for a (canonical) embedding model name.
 
     `embedding_model` is None when no embedding is configured for this run
@@ -172,7 +190,7 @@ def _model_dir_name(embedding_model: Optional[str]) -> str:
     return RegistryManager.safe_model_name(embedding_model)
 
 
-def _concept_info(kg: KnowledgeGraph, concept_id: int) -> Dict:
+def _concept_info(kg: KnowledgeGraph, concept_id: int) -> dict:
     try:
         cv = kg.concept_view(concept_id)
         return {
@@ -182,25 +200,27 @@ def _concept_info(kg: KnowledgeGraph, concept_id: int) -> Dict:
             "concept_code": cv.concept_code,
             "domain_id": cv.domain_id,
         }
-    except Exception:
+    except NoResultFound:
         return {"concept_id": concept_id, "concept_name": f"[concept {concept_id}]"}
 
 
 # ─── Core trace logic ─────────────────────────────────────────────────────────
 
+
 def _run_trace(
     kg: KnowledgeGraph,
     query: str,
     case_id: str,
-    parent_ids: Tuple[int, ...],
-    search_constraint: Optional[ConceptFilter],
-    expected_concept_id: Optional[int],
+    parent_ids: tuple[int, ...],
+    search_constraint: ConceptFilter | None,
+    expected_concept_id: int | None,
     top_n: int,
-) -> Dict:
+) -> dict:
     # Stage 0: compute query embedding once (shared by individual resolvers + ground_term)
     query_embedding = None
     try:
         from omop_emb.interface import EmbeddingRole
+
         writer = get_embedding_writer_interface(kg)
         if writer is not None:
             query_embedding = writer.embed_texts(texts=(query,), role=EmbeddingRole.QUERY)
@@ -208,28 +228,32 @@ def _run_trace(
         else:
             logger.info("No embedding writer available; EmbeddingResolver will be skipped.")
     except Exception as exc:
-        logger.warning("Could not compute query embedding: %s", exc)
+        logger.warning("Could not compute query embedding: %s", exc, exc_info=True)
 
     # Stage 1: run every resolver individually to capture per-resolver attribution
     resolver_stages = []
     seen: set = set()
-    concept_to_resolvers: Dict[int, List[str]] = {}
+    concept_to_resolvers: dict[int, list[str]] = {}
 
     for resolver in ORDERED_RESOLVERS:
         label = _resolver_label(resolver)
         hit_count = 0
         previously_unseen_hits = 0
         try:
-            for hit in resolver.resolve(kg=kg, query=query, constraints=search_constraint,
-                                        query_embedding=query_embedding):
+            for hit in resolver.resolve(
+                kg=kg, query=query, constraints=search_constraint, query_embedding=query_embedding
+            ):
                 hit_count += 1
                 if hit.concept_id not in seen:
                     previously_unseen_hits += 1
                     seen.add(hit.concept_id)
                 concept_to_resolvers.setdefault(hit.concept_id, []).append(label)
         except Exception as exc:
+            logger.warning("Resolver %s skipped", label, exc_info=True)
             console.print(f"  {label}: skipped ({exc})")
-        resolver_stages.append({"resolver": label, "hits": hit_count, "previously_unseen_hits": previously_unseen_hits})
+        resolver_stages.append(
+            {"resolver": label, "hits": hit_count, "previously_unseen_hits": previously_unseen_hits}
+        )
 
     # Stage 2: full grounding pipeline (hierarchy validation + scoring + embedding ranking)
     pipeline = ResolverPipeline(resolvers=tuple(ORDERED_RESOLVERS))
@@ -257,35 +281,45 @@ def _run_trace(
         info = _concept_info(kg, cid)
         if cid in scored_by_id:
             sc = scored_by_id[cid]
-            hierarchy_validation.append({
-                "candidate_concept_id": cid,
-                "candidate_concept_name": info["concept_name"],
-                "resolver": resolvers_list[0],
-                "resolvers": resolvers_list,
-                "passed": True,
-                "standard_concept_id": sc.concept_id,
-                "standard_concept_name": sc.concept_name,
-                "separation": sc.separation,
-                "parent_id": parent_ids[0] if parent_ids else None,
-            })
+            hierarchy_validation.append(
+                {
+                    "candidate_concept_id": cid,
+                    "candidate_concept_name": info["concept_name"],
+                    "resolver": resolvers_list[0],
+                    "resolvers": resolvers_list,
+                    "passed": True,
+                    "standard_concept_id": sc.concept_id,
+                    "standard_concept_name": sc.concept_name,
+                    "separation": sc.separation,
+                    "parent_id": parent_ids[0] if parent_ids else None,
+                }
+            )
         else:
-            hierarchy_validation.append({
-                "candidate_concept_id": cid,
-                "candidate_concept_name": info["concept_name"],
-                "resolver": resolvers_list[0],
-                "resolvers": resolvers_list,
-                "passed": False,
-            })
+            hierarchy_validation.append(
+                {
+                    "candidate_concept_id": cid,
+                    "candidate_concept_name": info["concept_name"],
+                    "resolver": resolvers_list[0],
+                    "resolvers": resolvers_list,
+                    "passed": False,
+                }
+            )
 
     if not scored:
         empty_group_scoring = []
         for group_name, members in RESOLVER_GROUPS:
             group_hits = sum(s["hits"] for s in resolver_stages if s["resolver"] in members)
-            empty_group_scoring.append({
-                "group": group_name, "members": list(members),
-                "hit_count": group_hits, "scored_count": 0,
-                "top1": None, "target_rank": None, "target_entry": None,
-            })
+            empty_group_scoring.append(
+                {
+                    "group": group_name,
+                    "members": list(members),
+                    "hit_count": group_hits,
+                    "scored_count": 0,
+                    "top1": None,
+                    "target_rank": None,
+                    "target_entry": None,
+                }
+            )
         return {
             "case_id": case_id,
             "query": query,
@@ -305,8 +339,9 @@ def _run_trace(
     resolver_scoring = []
     for resolver in ORDERED_RESOLVERS:
         label = _resolver_label(resolver)
-        resolver_subset = [sc for sc in scored
-                           if label in concept_to_resolvers.get(sc.concept_id, [])]
+        resolver_subset = [
+            sc for sc in scored if label in concept_to_resolvers.get(sc.concept_id, [])
+        ]
         if not resolver_subset:
             continue
         top1 = resolver_subset[0]
@@ -318,36 +353,49 @@ def _run_trace(
                     resolver_target_rank = i + 1
                     target_sc = sc
                     break
-        resolver_scoring.append({
-            "resolver": label,
-            "hit_count": sum(1 for rlist in concept_to_resolvers.values() if label in rlist),
-            "scored_count": len(resolver_subset),
-            "top1": {
-                "concept_id": top1.concept_id,
-                "concept_name": top1.concept_name,
-                "total_score": round(top1.total_score, 4),
-                "is_target": top1.concept_id == expected_concept_id,
-            },
-            "target_rank": resolver_target_rank,
-            "target_entry": {
-                "concept_id": target_sc.concept_id,
-                "concept_name": target_sc.concept_name,
-                "total_score": round(target_sc.total_score, 4),
-            } if target_sc and target_sc.concept_id != top1.concept_id else None,
-        })
+        resolver_scoring.append(
+            {
+                "resolver": label,
+                "hit_count": sum(1 for rlist in concept_to_resolvers.values() if label in rlist),
+                "scored_count": len(resolver_subset),
+                "top1": {
+                    "concept_id": top1.concept_id,
+                    "concept_name": top1.concept_name,
+                    "total_score": round(top1.total_score, 4),
+                    "is_target": top1.concept_id == expected_concept_id,
+                },
+                "target_rank": resolver_target_rank,
+                "target_entry": {
+                    "concept_id": target_sc.concept_id,
+                    "concept_name": target_sc.concept_name,
+                    "total_score": round(target_sc.total_score, 4),
+                }
+                if target_sc and target_sc.concept_id != top1.concept_id
+                else None,
+            }
+        )
 
     # Stage 2c: grouped view — synonym + non-synonym merged, for SVG columns
     resolver_group_scoring = []
     for group_name, members in RESOLVER_GROUPS:
-        group_subset = [sc for sc in scored
-                        if any(m in concept_to_resolvers.get(sc.concept_id, []) for m in members)]
+        group_subset = [
+            sc
+            for sc in scored
+            if any(m in concept_to_resolvers.get(sc.concept_id, []) for m in members)
+        ]
         group_hits = sum(s["hits"] for s in resolver_stages if s["resolver"] in members)
         if not group_subset:
-            resolver_group_scoring.append({
-                "group": group_name, "members": list(members),
-                "hit_count": group_hits, "scored_count": 0,
-                "top1": None, "target_rank": None, "target_entry": None,
-            })
+            resolver_group_scoring.append(
+                {
+                    "group": group_name,
+                    "members": list(members),
+                    "hit_count": group_hits,
+                    "scored_count": 0,
+                    "top1": None,
+                    "target_rank": None,
+                    "target_entry": None,
+                }
+            )
             continue
         top1 = group_subset[0]
         group_target_rank = None
@@ -358,27 +406,31 @@ def _run_trace(
                     group_target_rank = i + 1
                     target_sc = sc
                     break
-        resolver_group_scoring.append({
-            "group": group_name,
-            "members": list(members),
-            "hit_count": group_hits,
-            "scored_count": len(group_subset),
-            "top1": {
-                "concept_id": top1.concept_id,
-                "concept_name": top1.concept_name,
-                "total_score": round(top1.total_score, 4),
-                "is_target": top1.concept_id == expected_concept_id,
-            },
-            "target_rank": group_target_rank,
-            "target_entry": {
-                "concept_id": target_sc.concept_id,
-                "concept_name": target_sc.concept_name,
-                "total_score": round(target_sc.total_score, 4),
-            } if target_sc and target_sc.concept_id != top1.concept_id else None,
-        })
+        resolver_group_scoring.append(
+            {
+                "group": group_name,
+                "members": list(members),
+                "hit_count": group_hits,
+                "scored_count": len(group_subset),
+                "top1": {
+                    "concept_id": top1.concept_id,
+                    "concept_name": top1.concept_name,
+                    "total_score": round(top1.total_score, 4),
+                    "is_target": top1.concept_id == expected_concept_id,
+                },
+                "target_rank": group_target_rank,
+                "target_entry": {
+                    "concept_id": target_sc.concept_id,
+                    "concept_name": target_sc.concept_name,
+                    "total_score": round(target_sc.total_score, 4),
+                }
+                if target_sc and target_sc.concept_id != top1.concept_id
+                else None,
+            }
+        )
 
     # Stage 3: build output — annotate each result with which resolvers found it
-    target_rank: Optional[int] = None
+    target_rank: int | None = None
     if expected_concept_id is not None:
         for i, sc in enumerate(scored):
             if sc.concept_id == expected_concept_id:
@@ -387,45 +439,51 @@ def _run_trace(
 
     scoring_list = []
     for rank, sc in enumerate(scored[:top_n], start=1):
-        scoring_list.append({
-            "rank": rank,
-            "concept_id": sc.concept_id,
-            "concept_name": sc.concept_name,
-            "relevance": round(sc.relevance, 4),
-            "parsimony_penalty": round(sc.parsimony_penalty, 4),
-            "broadness_bonus": round(sc.broadness_bonus, 4),
-            "total_score": round(sc.total_score, 4),
-            "is_target": sc.concept_id == expected_concept_id,
-            "resolvers": concept_to_resolvers.get(sc.concept_id, []),
-        })
+        scoring_list.append(
+            {
+                "rank": rank,
+                "concept_id": sc.concept_id,
+                "concept_name": sc.concept_name,
+                "relevance": round(sc.relevance, 4),
+                "parsimony_penalty": round(sc.parsimony_penalty, 4),
+                "broadness_bonus": round(sc.broadness_bonus, 4),
+                "total_score": round(sc.total_score, 4),
+                "is_target": sc.concept_id == expected_concept_id,
+                "resolvers": concept_to_resolvers.get(sc.concept_id, []),
+            }
+        )
     # Always include target even when it falls outside top_n
     if expected_concept_id and target_rank and target_rank > top_n:
         sc = scored[target_rank - 1]
-        scoring_list.append({
-            "rank": target_rank,
-            "concept_id": sc.concept_id,
-            "concept_name": sc.concept_name,
-            "relevance": round(sc.relevance, 4),
-            "parsimony_penalty": round(sc.parsimony_penalty, 4),
-            "broadness_bonus": round(sc.broadness_bonus, 4),
-            "total_score": round(sc.total_score, 4),
-            "is_target": True,
-            "resolvers": concept_to_resolvers.get(sc.concept_id, []),
-        })
+        scoring_list.append(
+            {
+                "rank": target_rank,
+                "concept_id": sc.concept_id,
+                "concept_name": sc.concept_name,
+                "relevance": round(sc.relevance, 4),
+                "parsimony_penalty": round(sc.parsimony_penalty, 4),
+                "broadness_bonus": round(sc.broadness_bonus, 4),
+                "total_score": round(sc.total_score, 4),
+                "is_target": True,
+                "resolvers": concept_to_resolvers.get(sc.concept_id, []),
+            }
+        )
 
     top_n_results = []
     for entry in scoring_list:
         info = _concept_info(kg, entry["concept_id"])
-        top_n_results.append({
-            "rank": entry["rank"],
-            "concept_id": entry["concept_id"],
-            "concept_name": entry["concept_name"],
-            "vocabulary_id": info.get("vocabulary_id", ""),
-            "concept_code": info.get("concept_code", ""),
-            "total_score": entry["total_score"],
-            "is_target": entry["is_target"],
-            "resolvers": entry["resolvers"],
-        })
+        top_n_results.append(
+            {
+                "rank": entry["rank"],
+                "concept_id": entry["concept_id"],
+                "concept_name": entry["concept_name"],
+                "vocabulary_id": info.get("vocabulary_id", ""),
+                "concept_code": info.get("concept_code", ""),
+                "total_score": entry["total_score"],
+                "is_target": entry["is_target"],
+                "resolvers": entry["resolvers"],
+            }
+        )
 
     return {
         "case_id": case_id,
@@ -442,15 +500,15 @@ def _run_trace(
 
 
 def _constraints_dict(
-    sc: Optional[ConceptFilter],
-    parent_ids: Tuple[int, ...],
+    sc: ConceptFilter | None,
+    parent_ids: tuple[int, ...],
     kg: KnowledgeGraph,
-) -> Dict:
+) -> dict:
     parent_name = None
     if parent_ids:
         try:
             parent_name = kg.concept_view(parent_ids[0]).concept_name
-        except Exception:
+        except NoResultFound:
             pass
     return {
         "parent_ids": list(parent_ids),
@@ -463,7 +521,8 @@ def _constraints_dict(
 
 # ─── SVG generation ───────────────────────────────────────────────────────────
 
-def _svg(trace: Dict, title: str) -> str:
+
+def _svg(trace: dict, title: str) -> str:
     groups = trace.get("resolver_group_scoring", [])
     n = max(len(groups), 1)
 
@@ -483,32 +542,32 @@ def _svg(trace: Dict, title: str) -> str:
     ARROW_COL = "#495057"
     OUTPUT_BG = "#eaf2ff"
     OUTPUT_BORDER = "#1a3a5c"
-    COL_HDR_BG = "#1a6b2e"   # dark green for resolver column header bars
+    COL_HDR_BG = "#1a6b2e"  # dark green for resolver column header bars
     TEXT_DARK = "#212529"
     TEXT_MID = "#495057"
     TEXT_LIGHT = "#6c757d"
     TEXT_OK = "#1a6b2e"
     FONT = "system-ui, -apple-system, Arial, sans-serif"
 
-    FS_XS  = 10   # eyebrow labels ("INPUT", "GLOBAL OUTPUT")
-    FS_SM  = 12   # metadata / secondary text
-    FS_MID = 14   # normal body text / concept names
-    FS_HDR = 13   # blue bar primary label (group name)
+    FS_XS = 10  # eyebrow labels ("INPUT", "GLOBAL OUTPUT")
+    FS_SM = 12  # metadata / secondary text
+    FS_MID = 14  # normal body text / concept names
+    FS_HDR = 13  # blue bar primary label (group name)
 
     COL_GAP = 10
     col_w = (CW - (n - 1) * COL_GAP) // n
     col_xs = [MARGIN + i * (col_w + COL_GAP) for i in range(n)]
-    max_chars = max(8, col_w // 8)   # ~14px font ≈ 8px/char
+    max_chars = max(8, col_w // 8)  # ~14px font ≈ 8px/char
 
-    COL_HDR_H  = 86   # column header total (bar 38 + two stats lines + padding)
-    COL_BAR_H  = 38   # green bar inside column header
-    ARROW_H    = 22
-    BLOCK_H    = 80   # result block height
+    COL_HDR_H = 86  # column header total (bar 38 + two stats lines + padding)
+    COL_BAR_H = 38  # green bar inside column header
+    ARROW_H = 22
+    BLOCK_H = 80  # result block height
     INPUT_BAR_H = 48  # blue bar inside INPUT block
-    OUT_BAR_H  = 44   # blue bar inside GLOBAL OUTPUT block
+    OUT_BAR_H = 44  # blue bar inside GLOBAL OUTPUT block
 
-    parts: List[str] = []
-    y = 12   # small top margin; no title text
+    parts: list[str] = []
+    y = 12  # small top margin; no title text
 
     def rect(x, yy, w, h, fill, stroke, rx=6, sw=1.5):
         return f'<rect x="{x}" y="{yy}" width="{w}" height="{h}" rx="{rx}" fill="{fill}" stroke="{stroke}" stroke-width="{sw}"/>'
@@ -519,8 +578,8 @@ def text(x, yy, s, fs=FS_MID, fill=TEXT_DARK, anchor="start", bold=False):
 
     def col_arrow(cx, yy, h=ARROW_H):
         return (
-            f'<line x1="{cx}" y1="{yy}" x2="{cx}" y2="{yy+h-8}" stroke="{ARROW_COL}" stroke-width="1.5"/>'
-            f'<polygon points="{cx-5},{yy+h-8} {cx+5},{yy+h-8} {cx},{yy+h}" fill="{ARROW_COL}"/>'
+            f'<line x1="{cx}" y1="{yy}" x2="{cx}" y2="{yy + h - 8}" stroke="{ARROW_COL}" stroke-width="1.5"/>'
+            f'<polygon points="{cx - 5},{yy + h - 8} {cx + 5},{yy + h - 8} {cx},{yy + h}" fill="{ARROW_COL}"/>'
         )
 
     def trunc(s, n_chars):
@@ -532,55 +591,97 @@ def field_row(x, yy, fields):
         for i, (lbl, val) in enumerate(fields):
             if i > 0:
                 tspans.append(f'<tspan fill="{TEXT_LIGHT}">  |  </tspan>')
-            tspans.append(f'<tspan font-weight="bold" fill="{TEXT_DARK}">{escape(str(lbl))}</tspan>')
+            tspans.append(
+                f'<tspan font-weight="bold" fill="{TEXT_DARK}">{escape(str(lbl))}</tspan>'
+            )
             tspans.append(f'<tspan fill="{TEXT_MID}"> {escape(str(val))}</tspan>')
         return f'<text x="{x}" y="{yy}" font-family="{FONT}" font-size="{FS_SM}">{"".join(tspans)}</text>'
 
     constraints = trace.get("constraints", {})
     parent_id = (constraints.get("parent_ids") or [None])[0]
-    parent_name = constraints.get("parent_concept_name") or (f"concept {parent_id}" if parent_id else "—")
+    parent_name = constraints.get("parent_concept_name") or (
+        f"concept {parent_id}" if parent_id else "—"
+    )
 
     # Resolve expected concept for the TARGET block
-    target_result  = next((r for r in trace.get("top_n_results", []) if r.get("is_target")), None)
-    expected_name  = trace.get("expected_concept_name") or (target_result.get("concept_name") if target_result else None)
-    expected_id    = target_result.get("concept_id") if target_result else None
+    target_result = next((r for r in trace.get("top_n_results", []) if r.get("is_target")), None)
+    expected_name = trace.get("expected_concept_name") or (
+        target_result.get("concept_name") if target_result else None
+    )
+    expected_id = target_result.get("concept_id") if target_result else None
     has_target_blk = expected_name is not None or expected_id is not None
 
     # ── 1. INPUT + TARGET ──
-    input_h  = 104   # bar(48) + two body rows at 22px each + 12px padding
-    input_w  = int(CW * 0.58) if has_target_blk else CW
-    target_w = CW - input_w - 12   # 12px gap
+    input_h = 104  # bar(48) + two body rows at 22px each + 12px padding
+    input_w = int(CW * 0.58) if has_target_blk else CW
+    target_w = CW - input_w - 12  # 12px gap
 
     # INPUT outer box
     parts.append(rect(MARGIN, y, input_w, input_h, BODY_BG, HEADER_BG, rx=6, sw=2))
     # blue bar: rounded top, flat bottom
     parts.append(rect(MARGIN, y, input_w, INPUT_BAR_H, HEADER_BG, HEADER_BG, rx=6, sw=0))
-    parts.append(f'<rect x="{MARGIN}" y="{y + INPUT_BAR_H // 2}" width="{input_w}" height="{INPUT_BAR_H // 2}" fill="{HEADER_BG}"/>')
+    parts.append(
+        f'<rect x="{MARGIN}" y="{y + INPUT_BAR_H // 2}" width="{input_w}" height="{INPUT_BAR_H // 2}" fill="{HEADER_BG}"/>'
+    )
     # eyebrow + query in bar
     parts.append(text(MARGIN + 12, y + 17, "INPUT", FS_XS, HEADER_FG))
-    parts.append(text(MARGIN + 12, y + 37, trunc(f'"{trace.get("query", "")}"', input_w // 8), FS_MID, HEADER_FG, bold=True))
+    parts.append(
+        text(
+            MARGIN + 12,
+            y + 37,
+            trunc(f'"{trace.get("query", "")}"', input_w // 8),
+            FS_MID,
+            HEADER_FG,
+            bold=True,
+        )
+    )
     # body: single compact row
     domain_str = constraints.get("domain") or "—"
     vocabs_str = ", ".join(constraints.get("vocabularies") or [])
     parent_str = f"{parent_name}  (concept {parent_id})" if parent_id else "—"
-    parts.append(field_row(MARGIN + 14, y + INPUT_BAR_H + 20, [
-        ("Domain:", domain_str),
-        ("Vocabularies:", vocabs_str),
-    ]))
-    parts.append(field_row(MARGIN + 14, y + INPUT_BAR_H + 42, [
-        ("Parent:", parent_str),
-    ]))
+    parts.append(
+        field_row(
+            MARGIN + 14,
+            y + INPUT_BAR_H + 20,
+            [
+                ("Domain:", domain_str),
+                ("Vocabularies:", vocabs_str),
+            ],
+        )
+    )
+    parts.append(
+        field_row(
+            MARGIN + 14,
+            y + INPUT_BAR_H + 42,
+            [
+                ("Parent:", parent_str),
+            ],
+        )
+    )
 
     # TARGET block (golden, right of INPUT)
     if has_target_blk:
         tx = MARGIN + input_w + 12
         parts.append(rect(tx, y, target_w, input_h, AMBER_BG, AMBER_BORDER, rx=6, sw=2))
         parts.append(rect(tx, y, target_w, INPUT_BAR_H, AMBER_BORDER, AMBER_BORDER, rx=6, sw=0))
-        parts.append(f'<rect x="{tx}" y="{y + INPUT_BAR_H // 2}" width="{target_w}" height="{INPUT_BAR_H // 2}" fill="{AMBER_BORDER}"/>')
+        parts.append(
+            f'<rect x="{tx}" y="{y + INPUT_BAR_H // 2}" width="{target_w}" height="{INPUT_BAR_H // 2}" fill="{AMBER_BORDER}"/>'
+        )
         parts.append(text(tx + 12, y + 17, "TARGET", FS_XS, HEADER_FG))
-        parts.append(text(tx + 12, y + 37, trunc(expected_name or "—", target_w // 8), FS_MID, HEADER_FG, bold=True))
+        parts.append(
+            text(
+                tx + 12,
+                y + 37,
+                trunc(expected_name or "—", target_w // 8),
+                FS_MID,
+                HEADER_FG,
+                bold=True,
+            )
+        )
         if expected_id:
-            parts.append(field_row(tx + 14, y + INPUT_BAR_H + 22, [("Concept ID:", str(expected_id))]))
+            parts.append(
+                field_row(tx + 14, y + INPUT_BAR_H + 22, [("Concept ID:", str(expected_id))])
+            )
 
     y += input_h + 16
 
@@ -603,11 +704,15 @@ def field_row(x, yy, fields):
         parts.append(rect(cx, cy, col_w, COL_HDR_H, BODY_BG, hdr_border, rx=6, sw=1.5))
         if has_results:
             parts.append(rect(cx, cy, col_w, COL_BAR_H, COL_HDR_BG, COL_HDR_BG, rx=6, sw=0))
-            parts.append(f'<rect x="{cx}" y="{cy + COL_BAR_H // 2}" width="{col_w}" height="{COL_BAR_H // 2}" fill="{COL_HDR_BG}"/>')
+            parts.append(
+                f'<rect x="{cx}" y="{cy + COL_BAR_H // 2}" width="{col_w}" height="{COL_BAR_H // 2}" fill="{COL_HDR_BG}"/>'
+            )
             parts.append(text(ccx, cy + 25, label, FS_HDR, HEADER_FG, "middle", bold=True))
         else:
             parts.append(rect(cx, cy, col_w, COL_BAR_H, MISS_BG, MISS_BORDER, rx=6, sw=0))
-            parts.append(f'<rect x="{cx}" y="{cy + COL_BAR_H // 2}" width="{col_w}" height="{COL_BAR_H // 2}" fill="{MISS_BG}"/>')
+            parts.append(
+                f'<rect x="{cx}" y="{cy + COL_BAR_H // 2}" width="{col_w}" height="{COL_BAR_H // 2}" fill="{MISS_BG}"/>'
+            )
             parts.append(text(ccx, cy + 25, label, FS_HDR, TEXT_MID, "middle", bold=True))
         stats_y = cy + COL_BAR_H + 18
         parts.append(text(ccx, stats_y, f"{hits:,} candidates", FS_SM, TEXT_MID, "middle"))
@@ -627,7 +732,9 @@ def field_row(x, yy, fields):
         if not has_results:
             parts.append(rect(cx, cy, col_w, BLOCK_H, MISS_BG, MISS_BORDER, rx=5, sw=1))
             parts.append(text(ccx, cy + 36, "—", 16, MISS_BORDER, "middle", bold=True))
-            parts.append(text(ccx, cy + 60, "no results passed hierarchy", FS_XS, TEXT_LIGHT, "middle"))
+            parts.append(
+                text(ccx, cy + 60, "no results passed hierarchy", FS_XS, TEXT_LIGHT, "middle")
+            )
         else:
             top1 = group_entry.get("top1") or {}
             is_tgt = top1.get("is_target", False)
@@ -635,11 +742,40 @@ def field_row(x, yy, fields):
             b1_border = HIT_BORDER if is_tgt else "#adb5bd"
             rank_col = TEXT_OK if is_tgt else TEXT_DARK
             parts.append(rect(cx, cy, col_w, BLOCK_H, b1_fill, b1_border, rx=5, sw=1.5))
-            parts.append(text(ccx, cy + 22, f"Rank 1  |  {top1.get('concept_id', '')}", FS_SM, rank_col, "middle", bold=True))
-            parts.append(text(ccx, cy + 46, trunc(top1.get("concept_name", "—"), max_chars), FS_MID, TEXT_DARK, "middle"))
+            parts.append(
+                text(
+                    ccx,
+                    cy + 22,
+                    f"Rank 1  |  {top1.get('concept_id', '')}",
+                    FS_SM,
+                    rank_col,
+                    "middle",
+                    bold=True,
+                )
+            )
+            parts.append(
+                text(
+                    ccx,
+                    cy + 46,
+                    trunc(top1.get("concept_name", "—"), max_chars),
+                    FS_MID,
+                    TEXT_DARK,
+                    "middle",
+                )
+            )
             if group_entry.get("target_entry"):
                 trank = group_entry.get("target_rank", "?")
-                parts.append(text(ccx, cy + 66, f"Target Rank: {trank}", FS_SM, AMBER_BORDER, "middle", bold=True))
+                parts.append(
+                    text(
+                        ccx,
+                        cy + 66,
+                        f"Target Rank: {trank}",
+                        FS_SM,
+                        AMBER_BORDER,
+                        "middle",
+                        bold=True,
+                    )
+                )
         cy += BLOCK_H
 
         col_bottoms.append(cy)
@@ -655,18 +791,22 @@ def field_row(x, yy, fields):
     parts.append(rect(MARGIN, y, CW, out_h, OUTPUT_BG, OUTPUT_BORDER, rx=8, sw=3))
     # blue bar: rounded top, flat bottom
     parts.append(rect(MARGIN, y, CW, OUT_BAR_H, OUTPUT_BORDER, OUTPUT_BORDER, rx=8, sw=0))
-    parts.append(f'<rect x="{MARGIN}" y="{y + OUT_BAR_H // 2}" width="{CW}" height="{OUT_BAR_H // 2}" fill="{OUTPUT_BORDER}"/>')
+    parts.append(
+        f'<rect x="{MARGIN}" y="{y + OUT_BAR_H // 2}" width="{CW}" height="{OUT_BAR_H // 2}" fill="{OUTPUT_BORDER}"/>'
+    )
     parts.append(text(MARGIN + 14, y + 16, "GLOBAL OUTPUT", FS_XS, HEADER_FG))
     if top_out:
         name_full = top_out.get("concept_name", "")
-        parts.append(text(MARGIN + 14, y + 34, trunc(name_full, CW // 8), FS_MID, HEADER_FG, bold=True))
+        parts.append(
+            text(MARGIN + 14, y + 34, trunc(name_full, CW // 8), FS_MID, HEADER_FG, bold=True)
+        )
         rank_color = TEXT_OK if target_rank == 1 else (AMBER_BORDER if target_rank else "#b02a37")
-        rank_val   = str(target_rank) if target_rank else "not grounded"
-        tspans: List[str] = []
+        rank_val = str(target_rank) if target_rank else "not grounded"
+        tspans: list[str] = []
         for lbl, val in [
             ("Vocabulary:", top_out.get("vocabulary_id", "—")),
-            ("Code:",       top_out.get("concept_code", "—")),
-            ("Concept:",    str(top_out.get("concept_id", "—"))),
+            ("Code:", top_out.get("concept_code", "—")),
+            ("Concept:", str(top_out.get("concept_id", "—"))),
         ]:
             if tspans:
                 tspans.append(f'<tspan fill="{TEXT_LIGHT}">  |  </tspan>')
@@ -680,7 +820,15 @@ def field_row(x, yy, fields):
         )
     else:
         parts.append(text(MARGIN + 14, y + 34, "—", FS_MID, HEADER_FG, bold=True))
-        parts.append(text(MARGIN + 14, y + OUT_BAR_H + 24, "No result — query could not be grounded.", FS_SM, "#b02a37"))
+        parts.append(
+            text(
+                MARGIN + 14,
+                y + OUT_BAR_H + 24,
+                "No result — query could not be grounded.",
+                FS_SM,
+                "#b02a37",
+            )
+        )
 
     y += out_h + 20
 
@@ -705,7 +853,7 @@ def field_row(x, yy, fields):
 )
 
 
-def _relationship_classification(kg: KnowledgeGraph, concept_id: int) -> Dict:
+def _relationship_classification(kg: KnowledgeGraph, concept_id: int) -> dict:
     """Group a concept's outgoing edges by predicate_kind for the Panel A diagram.
 
     One representative edge (relationship + target concept name) per class, plus the total
@@ -713,12 +861,16 @@ def _relationship_classification(kg: KnowledgeGraph, concept_id: int) -> Dict:
     vs. excluded by default (everything else) without enumerating every edge.
     """
     edges = kg.edges(concept_ids=concept_id, direction="out", active_only=True, within_domain=False)
-    by_kind: Dict[PredicateKind, List] = {}
+    by_kind: dict[PredicateKind, list] = {}
     for edge in edges:
         by_kind.setdefault(edge.predicate_kind, []).append(edge)
 
     object_ids = tuple({edge.object_id for edge in edges})
-    names = {cv.concept_id: cv.concept_name for cv in kg.concept_views(object_ids)} if object_ids else {}
+    names = (
+        {cv.concept_id: cv.concept_name for cv in kg.concept_views(object_ids)}
+        if object_ids
+        else {}
+    )
 
     groups = []
     for kind in PREDICATE_KIND_ORDER:
@@ -727,22 +879,26 @@ def _relationship_classification(kg: KnowledgeGraph, concept_id: int) -> Dict:
             groups.append({"kind": kind.value, "count": 0, "example": None})
             continue
         rep = kind_edges[0]
-        groups.append({
-            "kind": kind.value,
-            "count": len(kind_edges),
-            "example": {
-                "predicate_id": rep.predicate_id,
-                "predicate_name": kg.predicate_name(rep.predicate_id),
-                "object_id": rep.object_id,
-                "object_name": names.get(rep.object_id, f"[concept {rep.object_id}]"),
-            },
-        })
+        groups.append(
+            {
+                "kind": kind.value,
+                "count": len(kind_edges),
+                "example": {
+                    "predicate_id": rep.predicate_id,
+                    "predicate_name": kg.predicate_name(rep.predicate_id),
+                    "object_id": rep.object_id,
+                    "object_name": names.get(rep.object_id, f"[concept {rep.object_id}]"),
+                },
+            }
+        )
 
     concept_name = kg.concept_view(concept_id).concept_name
     return {"concept_id": concept_id, "concept_name": concept_name, "groups": groups}
 
 
-def _pick_hierarchy_examples(hierarchy_validation: List[Dict], passed: bool, limit: int = 3) -> List[Dict]:
+def _pick_hierarchy_examples(
+    hierarchy_validation: list[dict], passed: bool, limit: int = 3
+) -> list[dict]:
     """Pick up to `limit` distinct-by-name hierarchy_validation entries for Panel B.
 
     For failures, Embedding-only hits are surfaced first: an Embedding candidate that falls
@@ -766,7 +922,7 @@ def _pick_hierarchy_examples(hierarchy_validation: List[Dict], passed: bool, lim
     return picked
 
 
-def _panel_svg(rel_class: Dict, trace: Dict, title: str) -> str:
+def _panel_svg(rel_class: dict, trace: dict, title: str) -> str:
     """Render the two-panel dashboard: Panel A (relationship classification / edge filtering)
     above Panel B (hierarchy-constrained candidate culling)."""
     MARGIN = 30
@@ -783,8 +939,8 @@ def _panel_svg(rel_class: Dict, trace: Dict, title: str) -> str:
     MISS_BORDER = "#ced4da"
     FAIL_BORDER = "#b02a37"
     ARROW_COL = "#495057"
-    COL_HDR_BG_WALK = "#1a6b2e"     # green: walkable for grounding (Identity)
-    COL_HDR_BG_EXCL = "#6c757d"    # gray: excluded by default (everything else)
+    COL_HDR_BG_WALK = "#1a6b2e"  # green: walkable for grounding (Identity)
+    COL_HDR_BG_EXCL = "#6c757d"  # gray: excluded by default (everything else)
     TEXT_DARK = "#212529"
     TEXT_MID = "#495057"
     TEXT_LIGHT = "#6c757d"
@@ -792,7 +948,7 @@ def _panel_svg(rel_class: Dict, trace: Dict, title: str) -> str:
 
     FS_XS, FS_SM, FS_MID, FS_HDR = 10, 12, 14, 13
 
-    parts: List[str] = []
+    parts: list[str] = []
 
     def rect(x, yy, w, h, fill, stroke, rx=6, sw=1.5):
         return f'<rect x="{x}" y="{yy}" width="{w}" height="{h}" rx="{rx}" fill="{fill}" stroke="{stroke}" stroke-width="{sw}"/>'
@@ -807,26 +963,52 @@ def trunc(s, n_chars):
     def fan_arrows(src_cx, src_y, col_xs, col_w, dst_y):
         """Draw a bus line fanning out from one source point down into N columns."""
         bus_y = src_y + (dst_y - src_y) // 2
-        out = [f'<line x1="{src_cx}" y1="{src_y}" x2="{src_cx}" y2="{bus_y}" stroke="{ARROW_COL}" stroke-width="1.5"/>']
+        out = [
+            f'<line x1="{src_cx}" y1="{src_y}" x2="{src_cx}" y2="{bus_y}" stroke="{ARROW_COL}" stroke-width="1.5"/>'
+        ]
         col_centers = [cx + col_w // 2 for cx in col_xs]
-        out.append(f'<line x1="{min(col_centers)}" y1="{bus_y}" x2="{max(col_centers)}" y2="{bus_y}" stroke="{ARROW_COL}" stroke-width="1.5"/>')
+        out.append(
+            f'<line x1="{min(col_centers)}" y1="{bus_y}" x2="{max(col_centers)}" y2="{bus_y}" stroke="{ARROW_COL}" stroke-width="1.5"/>'
+        )
         for ccx in col_centers:
-            out.append(f'<line x1="{ccx}" y1="{bus_y}" x2="{ccx}" y2="{dst_y-8}" stroke="{ARROW_COL}" stroke-width="1.5"/>')
-            out.append(f'<polygon points="{ccx-5},{dst_y-8} {ccx+5},{dst_y-8} {ccx},{dst_y}" fill="{ARROW_COL}"/>')
+            out.append(
+                f'<line x1="{ccx}" y1="{bus_y}" x2="{ccx}" y2="{dst_y - 8}" stroke="{ARROW_COL}" stroke-width="1.5"/>'
+            )
+            out.append(
+                f'<polygon points="{ccx - 5},{dst_y - 8} {ccx + 5},{dst_y - 8} {ccx},{dst_y}" fill="{ARROW_COL}"/>'
+            )
         return "\n".join(out)
 
     y = 12
 
     # ── Panel A: relationship classification ──
-    parts.append(text(MARGIN, y + 8, "PANEL A — Relationship classification (edge filtering)", FS_SM, TEXT_LIGHT, bold=True))
+    parts.append(
+        text(
+            MARGIN,
+            y + 8,
+            "PANEL A — Relationship classification (edge filtering)",
+            FS_SM,
+            TEXT_LIGHT,
+            bold=True,
+        )
+    )
     y += 18
 
     node_h = 56
     parts.append(rect(MARGIN, y, CW, node_h, BODY_BG, HEADER_BG, rx=6, sw=2))
     parts.append(rect(MARGIN, y, CW, 28, HEADER_BG, HEADER_BG, rx=6, sw=0))
-    parts.append(f'<rect x="{MARGIN}" y="{y+14}" width="{CW}" height="14" fill="{HEADER_BG}"/>')
+    parts.append(f'<rect x="{MARGIN}" y="{y + 14}" width="{CW}" height="14" fill="{HEADER_BG}"/>')
     parts.append(text(MARGIN + 12, y + 19, "CONCEPT", FS_XS, HEADER_FG))
-    parts.append(text(MARGIN + 12, y + 48, trunc(f'{rel_class["concept_name"]}  (concept {rel_class["concept_id"]})', CW // 8), FS_MID, TEXT_DARK, bold=True))
+    parts.append(
+        text(
+            MARGIN + 12,
+            y + 48,
+            trunc(f"{rel_class['concept_name']}  (concept {rel_class['concept_id']})", CW // 8),
+            FS_MID,
+            TEXT_DARK,
+            bold=True,
+        )
+    )
     concept_box_bottom = y + node_h
     concept_cx = MARGIN + CW // 2
     y = concept_box_bottom + 36
@@ -851,12 +1033,14 @@ def fan_arrows(src_cx, src_y, col_xs, col_w, dst_y):
 
         parts.append(rect(cx, y, col_w, col_h, body_bg, body_border, rx=6, sw=1.5))
         parts.append(rect(cx, y, col_w, 30, hdr_bg, hdr_bg, rx=6, sw=0))
-        parts.append(f'<rect x="{cx}" y="{y+15}" width="{col_w}" height="15" fill="{hdr_bg}"/>')
+        parts.append(f'<rect x="{cx}" y="{y + 15}" width="{col_w}" height="15" fill="{hdr_bg}"/>')
         parts.append(text(ccx, y + 20, group["kind"], FS_HDR, HEADER_FG, "middle", bold=True))
-        parts.append(text(ccx, y + 46, f'{group["count"]} edges', FS_SM, TEXT_MID, "middle"))
+        parts.append(text(ccx, y + 46, f"{group['count']} edges", FS_SM, TEXT_MID, "middle"))
         if group["example"]:
             ex = group["example"]
-            edge_label = trunc(f'-[{ex["predicate_name"]}]-> {ex["object_name"]}', max(8, col_w // 7))
+            edge_label = trunc(
+                f"-[{ex['predicate_name']}]-> {ex['object_name']}", max(8, col_w // 7)
+            )
             parts.append(text(ccx, y + 66, edge_label, FS_SM, TEXT_DARK, "middle"))
         else:
             parts.append(text(ccx, y + 66, "no edges", FS_SM, TEXT_LIGHT, "middle"))
@@ -869,17 +1053,30 @@ def fan_arrows(src_cx, src_y, col_xs, col_w, dst_y):
     # ── Panel B: hierarchy-constrained culling ──
     constraints = trace.get("constraints", {})
     parent_id = (constraints.get("parent_ids") or [None])[0]
-    parent_name = constraints.get("parent_concept_name") or (f"concept {parent_id}" if parent_id else "—")
+    parent_name = constraints.get("parent_concept_name") or (
+        f"concept {parent_id}" if parent_id else "—"
+    )
 
-    parts.append(text(MARGIN, y + 8, "PANEL B — Hierarchy-constrained culling", FS_SM, TEXT_LIGHT, bold=True))
+    parts.append(
+        text(MARGIN, y + 8, "PANEL B — Hierarchy-constrained culling", FS_SM, TEXT_LIGHT, bold=True)
+    )
     y += 18
 
     anchor_h = 56
     parts.append(rect(MARGIN, y, CW, anchor_h, BODY_BG, HEADER_BG, rx=6, sw=2))
     parts.append(rect(MARGIN, y, CW, 28, HEADER_BG, HEADER_BG, rx=6, sw=0))
-    parts.append(f'<rect x="{MARGIN}" y="{y+14}" width="{CW}" height="14" fill="{HEADER_BG}"/>')
+    parts.append(f'<rect x="{MARGIN}" y="{y + 14}" width="{CW}" height="14" fill="{HEADER_BG}"/>')
     parts.append(text(MARGIN + 12, y + 19, "ANCHOR", FS_XS, HEADER_FG))
-    parts.append(text(MARGIN + 12, y + 48, trunc(f'{parent_name}  (concept {parent_id})' if parent_id else "—", CW // 8), FS_MID, TEXT_DARK, bold=True))
+    parts.append(
+        text(
+            MARGIN + 12,
+            y + 48,
+            trunc(f"{parent_name}  (concept {parent_id})" if parent_id else "—", CW // 8),
+            FS_MID,
+            TEXT_DARK,
+            bold=True,
+        )
+    )
     anchor_box_bottom = y + anchor_h
     anchor_cx = MARGIN + CW // 2
     y = anchor_box_bottom + 36
@@ -895,28 +1092,36 @@ def fan_arrows(src_cx, src_y, col_xs, col_w, dst_y):
 
     parts.append(fan_arrows(anchor_cx, anchor_box_bottom, pcol_xs, pcol_w, y))
 
-    for col_idx, (label, examples, is_pass) in enumerate((
-        ("PASSED hierarchy anchor", pass_examples, True),
-        ("FAILED hierarchy anchor", fail_examples, False),
-    )):
+    for col_idx, (label, examples, is_pass) in enumerate(
+        (
+            ("PASSED hierarchy anchor", pass_examples, True),
+            ("FAILED hierarchy anchor", fail_examples, False),
+        )
+    ):
         cx = pcol_xs[col_idx]
         ccx = cx + pcol_w // 2
         hdr_bg = HIT_BORDER if is_pass else FAIL_BORDER
         body_border = HIT_BORDER if is_pass else FAIL_BORDER
         parts.append(rect(cx, y, pcol_w, pcol_h, "#ffffff", body_border, rx=6, sw=1.5))
         parts.append(rect(cx, y, pcol_w, 28, hdr_bg, hdr_bg, rx=6, sw=0))
-        parts.append(f'<rect x="{cx}" y="{y+14}" width="{pcol_w}" height="14" fill="{hdr_bg}"/>')
+        parts.append(f'<rect x="{cx}" y="{y + 14}" width="{pcol_w}" height="14" fill="{hdr_bg}"/>')
         parts.append(text(ccx, y + 19, label, FS_HDR, HEADER_FG, "middle", bold=True))
         if not examples:
-            parts.append(text(ccx, y + 28 + row_h // 2 + 4, "no examples", FS_SM, TEXT_LIGHT, "middle"))
+            parts.append(
+                text(ccx, y + 28 + row_h // 2 + 4, "no examples", FS_SM, TEXT_LIGHT, "middle")
+            )
         for ri, entry in enumerate(examples):
             ry = y + 28 + ri * row_h + row_h // 2
             name = trunc(entry.get("candidate_concept_name", ""), max(8, pcol_w // 7))
             cid = entry.get("candidate_concept_id")
             resolver = entry.get("resolver", "?")
             fill = TEXT_OK if is_pass else FAIL_BORDER
-            parts.append(text(cx + 10, ry, f'{name}  ({cid})', FS_SM, TEXT_DARK, bold=False))
-            detail = f'sep {entry["separation"]} | found by: {resolver}' if is_pass else f'found by: {resolver} | outside anchor'
+            parts.append(text(cx + 10, ry, f"{name}  ({cid})", FS_SM, TEXT_DARK, bold=False))
+            detail = (
+                f"sep {entry['separation']} | found by: {resolver}"
+                if is_pass
+                else f"found by: {resolver} | outside anchor"
+            )
             parts.append(text(cx + 10, ry + 15, detail, FS_XS, fill))
 
     y += pcol_h + 24
@@ -940,14 +1145,14 @@ def fan_arrows(src_cx, src_y, col_xs, col_w, dst_y):
 }
 
 
-def _resolver_groups_for(resolvers: List[str]) -> List[str]:
+def _resolver_groups_for(resolvers: list[str]) -> list[str]:
     """Map a candidate's full resolver list to its distinct, canonically-ordered group names
     (e.g. ["Exact", "Exact Synonym"] -> ["Exact"]; preserves RESOLVER_GROUPS order)."""
     resolver_set = set(resolvers)
     return [group_name for group_name, members in RESOLVER_GROUPS if resolver_set & set(members)]
 
 
-def _resolve_identity_hop(kg: KnowledgeGraph, candidate_id: int, standard_id: Optional[int]) -> Dict:
+def _resolve_identity_hop(kg: KnowledgeGraph, candidate_id: int, standard_id: int | None) -> dict:
     """Resolve the real Identity edge (if any) a candidate uses to reach a standard concept.
 
     Returns ``{"already_standard": bool, "predicate_name": str|None, "object_id": int|None,
@@ -963,12 +1168,20 @@ def _resolve_identity_hop(kg: KnowledgeGraph, candidate_id: int, standard_id: Op
     cv = kg.concept_view(candidate_id)
     base = {"vocabulary_id": cv.vocabulary_id, "invalid_reason": cv.invalid_reason}
     if cv.standard_concept:
-        return {**base, "already_standard": True, "predicate_name": None, "object_id": None, "object_name": None}
+        return {
+            **base,
+            "already_standard": True,
+            "predicate_name": None,
+            "object_id": None,
+            "object_name": None,
+        }
 
     edges = kg.edges(
-        concept_ids=candidate_id, direction="out",
+        concept_ids=candidate_id,
+        direction="out",
         predicate_kinds=frozenset({PredicateKind.IDENTITY}),
-        active_only=True, within_domain=False,
+        active_only=True,
+        within_domain=False,
     )
     target_edge = None
     if standard_id is not None:
@@ -976,7 +1189,13 @@ def _resolve_identity_hop(kg: KnowledgeGraph, candidate_id: int, standard_id: Op
     if target_edge is None and edges:
         target_edge = edges[0]
     if target_edge is None:
-        return {**base, "already_standard": False, "predicate_name": None, "object_id": None, "object_name": None}
+        return {
+            **base,
+            "already_standard": False,
+            "predicate_name": None,
+            "object_id": None,
+            "object_name": None,
+        }
 
     object_name = kg.concept_view(target_edge.object_id).concept_name
     return {
@@ -988,7 +1207,7 @@ def _resolve_identity_hop(kg: KnowledgeGraph, candidate_id: int, standard_id: Op
     }
 
 
-def _select_funnel_candidates(trace: Dict, max_passed: int = 6, max_failed: int = 12) -> List[Dict]:
+def _select_funnel_candidates(trace: dict, max_passed: int = 6, max_failed: int = 12) -> list[dict]:
     """Pick a small, representative set of `hierarchy_validation` entries for the funnel
     diagram's starting (stage 1) column -- enough to show real candidates getting eliminated
     at each stage, without enumerating every candidate the resolvers found.
@@ -1007,7 +1226,7 @@ def _select_funnel_candidates(trace: Dict, max_passed: int = 6, max_failed: int
     passed = [e for e in hierarchy_validation if e.get("passed")]
     failed = [e for e in hierarchy_validation if not e.get("passed")]
 
-    by_standard: Dict[int, List[Dict]] = {}
+    by_standard: dict[int, list[dict]] = {}
     for e in passed:
         by_standard.setdefault(e["standard_concept_id"], []).append(e)
 
@@ -1027,7 +1246,9 @@ def _select_funnel_candidates(trace: Dict, max_passed: int = 6, max_failed: int
     selected_passed = ([winner_rep] if winner_rep else []) + other_reps
     selected_passed = selected_passed[:max_passed]
 
-    failed_sorted = sorted(failed, key=lambda e: "Embedding" not in (e.get("resolvers") or [e.get("resolver")]))
+    failed_sorted = sorted(
+        failed, key=lambda e: "Embedding" not in (e.get("resolvers") or [e.get("resolver")])
+    )
     # Exclude failed candidates sharing a name with an already-selected passed one: these are
     # almost always the same surface form under a different (duplicate vocabulary source)
     # concept ID, which renders confusingly -- a "failed" lane whose live Identity-edge lookup
@@ -1048,7 +1269,7 @@ def _select_funnel_candidates(trace: Dict, max_passed: int = 6, max_failed: int
     return selected_passed + selected_failed
 
 
-def _build_funnel_nodes(kg: KnowledgeGraph, trace: Dict, entries: List[Dict]) -> List[Dict]:
+def _build_funnel_nodes(kg: KnowledgeGraph, trace: dict, entries: list[dict]) -> list[dict]:
     """Resolve each selected candidate's real Identity hop and attach resolver/score metadata,
     ready for the funnel renderer."""
     top_n_results = trace.get("top_n_results", [])
@@ -1075,21 +1296,27 @@ def _build_funnel_nodes(kg: KnowledgeGraph, trace: Dict, entries: List[Dict]) ->
 
         resolvers = entry.get("resolvers") or [entry.get("resolver")]
         assert isinstance(resolvers, list) and all(isinstance(r, str) for r in resolvers)
-        nodes.append({
-            "candidate_id": candidate_id,
-            "candidate_name": candidate_name,
-            "candidate_vocabulary_id": hop["vocabulary_id"],
-            "candidate_invalid_reason": hop["invalid_reason"],
-            "resolver_groups": _resolver_groups_for(resolvers),  # type: ignore
-            "already_standard": hop["already_standard"],
-            "edge_predicate": hop["predicate_name"],
-            "standard_id": final_standard_id,
-            "standard_name": final_standard_name,
-            "passed": passed,
-            "separation": entry.get("separation") if passed else None,
-            "score": score_by_id.get(final_standard_id) if final_standard_id is not None else None,
-            "is_winner": passed and final_standard_id is not None and final_standard_id == winner_id,
-        })
+        nodes.append(
+            {
+                "candidate_id": candidate_id,
+                "candidate_name": candidate_name,
+                "candidate_vocabulary_id": hop["vocabulary_id"],
+                "candidate_invalid_reason": hop["invalid_reason"],
+                "resolver_groups": _resolver_groups_for(resolvers),  # type: ignore
+                "already_standard": hop["already_standard"],
+                "edge_predicate": hop["predicate_name"],
+                "standard_id": final_standard_id,
+                "standard_name": final_standard_name,
+                "passed": passed,
+                "separation": entry.get("separation") if passed else None,
+                "score": score_by_id.get(final_standard_id)
+                if final_standard_id is not None
+                else None,
+                "is_winner": passed
+                and final_standard_id is not None
+                and final_standard_id == winner_id,
+            }
+        )
 
     # Drop failed nodes whose live-Identity-edge fallback happens to land on a standard concept
     # that another lane independently confirmed *passes* the anchor -- internally contradictory
@@ -1100,7 +1327,9 @@ def _build_funnel_nodes(kg: KnowledgeGraph, trace: Dict, entries: List[Dict]) ->
     return nodes
 
 
-def _trim_funnel_nodes(nodes: List[Dict], max_passed: int = 3, max_no_mapping: int = 1, max_hierarchy_fail: int = 1) -> List[Dict]:
+def _trim_funnel_nodes(
+    nodes: list[dict], max_passed: int = 3, max_no_mapping: int = 1, max_hierarchy_fail: int = 1
+) -> list[dict]:
     """Cut the built node list down to a small, clean set for the figure: the winner plus a
     couple of other genuinely-scored alternatives, and one example of each failure flavor (no
     Identity mapping at all vs. a real mapping that falls outside the hierarchy anchor) -- so
@@ -1120,12 +1349,14 @@ def _trim_funnel_nodes(nodes: List[Dict], max_passed: int = 3, max_no_mapping: i
         (n for n in nodes if not n["passed"] and n["standard_id"] is None),
         key=lambda n: n["candidate_invalid_reason"] is not None,
     )[:max_no_mapping]
-    hierarchy_fail = [n for n in nodes if not n["passed"] and n["standard_id"] is not None][:max_hierarchy_fail]
+    hierarchy_fail = [n for n in nodes if not n["passed"] and n["standard_id"] is not None][
+        :max_hierarchy_fail
+    ]
 
     return selected_passed + no_mapping + hierarchy_fail
 
 
-def _resolve_anchor_concepts(kg: KnowledgeGraph, trace: Dict) -> List[Dict]:
+def _resolve_anchor_concepts(kg: KnowledgeGraph, trace: dict) -> list[dict]:
     """Resolve every hierarchy-anchor parent ID's concept name (not just the first) for the
     funnel diagram's top banner."""
     parent_ids = trace.get("constraints", {}).get("parent_ids") or []
@@ -1133,10 +1364,13 @@ def _resolve_anchor_concepts(kg: KnowledgeGraph, trace: Dict) -> List[Dict]:
         return []
     views = kg.concept_views(tuple(parent_ids))
     name_by_id = {v.concept_id: v.concept_name for v in views}
-    return [{"concept_id": pid, "concept_name": name_by_id.get(pid, f"[concept {pid}]")} for pid in parent_ids]
+    return [
+        {"concept_id": pid, "concept_name": name_by_id.get(pid, f"[concept {pid}]")}
+        for pid in parent_ids
+    ]
 
 
-def _funnel_svg(nodes: List[Dict], trace: Dict, anchor_concepts: List[Dict]) -> str:
+def _funnel_svg(nodes: list[dict], trace: dict, anchor_concepts: list[dict]) -> str:
     """Render the 4-stage left-to-right funnel: candidates -> standard concepts (Identity hop)
     -> hierarchy-validated -> scored/winner. Candidates are stacked vertically; nodes that
     converge on the same standard concept merge into one box in later stages. Eliminated
@@ -1185,11 +1419,17 @@ def wrap_words(s, max_chars, max_lines=2):
     # ── Hierarchy anchor banner sizing (computed up front so the grid leaves it enough room) ──
     ANCHOR_LABEL = "Hierarchy anchor: "
     if anchor_concepts:
-        anchor_value = ", ".join(f'{a["concept_name"]} [{a["concept_id"]}]' for a in anchor_concepts)
+        anchor_value = ", ".join(
+            f"{a['concept_name']} [{a['concept_id']}]" for a in anchor_concepts
+        )
     else:
         anchor_value = "—"
     anchor_chars_per_line = max(20, int((W - 2 * MARGIN - 24) / 7.3))
-    anchor_lines = [line for line in wrap_words(ANCHOR_LABEL + anchor_value, anchor_chars_per_line, max_lines=4) if line] or ["—"]
+    anchor_lines = [
+        line
+        for line in wrap_words(ANCHOR_LABEL + anchor_value, anchor_chars_per_line, max_lines=4)
+        if line
+    ] or ["—"]
     ANCHOR_BOX_H = 16 + len(anchor_lines) * ANCHOR_LINE_H
 
     # Anchor banner sits directly under the search-term banner with no gap; headers/grid start
@@ -1198,16 +1438,16 @@ def wrap_words(s, max_chars, max_lines=2):
     ANCHOR_BOX_Y = SEARCH_BOX_Y + SEARCH_BOX_H
     HEADER_Y = ANCHOR_BOX_Y + ANCHOR_BOX_H + 24
     TOP_Y = HEADER_Y + 9
-    BOTTOM_MARGIN = 7        # gap below the "Found by" legend to the canvas edge
-    LEGEND_RECT_H = 14       # the colored resolver-swatch height in the legend
-    GRID_LEGEND_GAP = 16     # gap between the last candidate row and the "Found by" legend
+    BOTTOM_MARGIN = 7  # gap below the "Found by" legend to the canvas edge
+    LEGEND_RECT_H = 14  # the colored resolver-swatch height in the legend
+    GRID_LEGEND_GAP = 16  # gap between the last candidate row and the "Found by" legend
     FOOTER_RESERVED = GRID_LEGEND_GAP + LEGEND_RECT_H + BOTTOM_MARGIN
 
     n = max(len(nodes), 1)
     total_h = n * NODE_H + (n - 1) * NODE_GAP
     H = max(420, TOP_Y + total_h + FOOTER_RESERVED)
 
-    parts: List[str] = []
+    parts: list[str] = []
 
     def rect(x, yy, w, h, fill, stroke, rx=6, sw=1.5, dashed=False):
         dash = ' stroke-dasharray="6,4"' if dashed else ""
@@ -1223,7 +1463,7 @@ def text_label_value(x, yy, label, value, fs=FS_SM, fill=TEXT_DARK):
             f'<text x="{x:.1f}" y="{yy:.1f}" font-family="{FONT}" font-size="{fs}" fill="{fill}" text-anchor="start">'
             f'<tspan font-weight="bold">{escape(str(label))}</tspan>'
             f'<tspan font-weight="normal">{escape(str(value))}</tspan>'
-            f'</text>'
+            f"</text>"
         )
 
     def split_label(label):
@@ -1244,11 +1484,15 @@ def arrow(x1, y1, x2, y2, color, dashed=False, bold=False, label=None):
         ux, uy = dx / dist, dy / dist
         head = 8
         lx2, ly2 = x2 - ux * head, y2 - uy * head
-        out = [f'<line x1="{x1:.1f}" y1="{y1:.1f}" x2="{lx2:.1f}" y2="{ly2:.1f}" stroke="{color}" stroke-width="{sw}"{dash}/>']
+        out = [
+            f'<line x1="{x1:.1f}" y1="{y1:.1f}" x2="{lx2:.1f}" y2="{ly2:.1f}" stroke="{color}" stroke-width="{sw}"{dash}/>'
+        ]
         px, py = -uy, ux
         p1 = (x2 - ux * head + px * 4, y2 - uy * head + py * 4)
         p2 = (x2 - ux * head - px * 4, y2 - uy * head - py * 4)
-        out.append(f'<polygon points="{p1[0]:.1f},{p1[1]:.1f} {p2[0]:.1f},{p2[1]:.1f} {x2:.1f},{y2:.1f}" fill="{color}"/>')
+        out.append(
+            f'<polygon points="{p1[0]:.1f},{p1[1]:.1f} {p2[0]:.1f},{p2[1]:.1f} {x2:.1f},{y2:.1f}" fill="{color}"/>'
+        )
         if label:
             mx, my = (x1 + lx2) / 2, (y1 + ly2) / 2
             if isinstance(label, tuple):
@@ -1268,7 +1512,9 @@ def stripe(x, yy, w, groups):
         out = []
         for i, g in enumerate(groups):
             color = RESOLVER_STRIPE_COLORS.get(g, TEXT_LIGHT)
-            out.append(f'<rect x="{x + i*seg_w:.1f}" y="{yy:.1f}" width="{seg_w:.1f}" height="6" fill="{color}"/>')
+            out.append(
+                f'<rect x="{x + i * seg_w:.1f}" y="{yy:.1f}" width="{seg_w:.1f}" height="6" fill="{color}"/>'
+            )
         return "\n".join(out)
 
     def name_id_box(x, y, name, cid):
@@ -1320,23 +1566,58 @@ def outcome_colors(passed, winner):
 
     # ── Search-term banner (full width, at the very top) ──
     query = trace.get("query") or "—"
-    parts.append(rect(MARGIN, SEARCH_BOX_Y, W - 2 * MARGIN, SEARCH_BOX_H, SEARCH_BG, SEARCH_BORDER, rx=6, sw=1.5))
-    parts.append(text_label_value(
-        MARGIN + 12, SEARCH_BOX_Y + SEARCH_BOX_H / 2 + 4, "Search term: ", f'"{query}"', FS_SM, SEARCH_BORDER,
-    ))
+    parts.append(
+        rect(
+            MARGIN,
+            SEARCH_BOX_Y,
+            W - 2 * MARGIN,
+            SEARCH_BOX_H,
+            SEARCH_BG,
+            SEARCH_BORDER,
+            rx=6,
+            sw=1.5,
+        )
+    )
+    parts.append(
+        text_label_value(
+            MARGIN + 12,
+            SEARCH_BOX_Y + SEARCH_BOX_H / 2 + 4,
+            "Search term: ",
+            f'"{query}"',
+            FS_SM,
+            SEARCH_BORDER,
+        )
+    )
 
     # ── Hierarchy anchor banner (full width, directly below the search-term banner, no gap) ──
-    parts.append(rect(MARGIN, ANCHOR_BOX_Y, W - 2 * MARGIN, ANCHOR_BOX_H, ANCHOR_BG, ANCHOR_BORDER, rx=6, sw=1.5))
+    parts.append(
+        rect(
+            MARGIN,
+            ANCHOR_BOX_Y,
+            W - 2 * MARGIN,
+            ANCHOR_BOX_H,
+            ANCHOR_BG,
+            ANCHOR_BORDER,
+            rx=6,
+            sw=1.5,
+        )
+    )
     yy = ANCHOR_BOX_Y + 8 + 12  # 8px top padding + ~12px baseline-from-top for a 12px font
     for i, line in enumerate(anchor_lines):
         if i == 0 and line.startswith(ANCHOR_LABEL):
-            parts.append(text_label_value(MARGIN + 12, yy, ANCHOR_LABEL, line[len(ANCHOR_LABEL):], FS_SM, ANCHOR_BORDER))
+            parts.append(
+                text_label_value(
+                    MARGIN + 12, yy, ANCHOR_LABEL, line[len(ANCHOR_LABEL) :], FS_SM, ANCHOR_BORDER
+                )
+            )
         else:
             parts.append(text(MARGIN + 12, yy, line, FS_SM, ANCHOR_BORDER))
         yy += ANCHOR_LINE_H
 
     for i, hdr in enumerate(headers):
-        parts.append(text(col_xs[i] + COL_W / 2, HEADER_Y, hdr, FS_SM, TEXT_LIGHT, "middle", bold=True))
+        parts.append(
+            text(col_xs[i] + COL_W / 2, HEADER_Y, hdr, FS_SM, TEXT_LIGHT, "middle", bold=True)
+        )
 
     n_nodes = len(nodes)
     start_y = TOP_Y + max(0, (H - TOP_Y - FOOTER_RESERVED - total_h) / 2)
@@ -1350,17 +1631,23 @@ def outcome_colors(passed, winner):
         parts.append(stripe(x, y, COL_W, node["resolver_groups"]))
         box_parts, next_y = name_id_box(x, y, node["candidate_name"], node["candidate_id"])
         parts.extend(box_parts)
-        parts.append(text(x + 10, next_y, f'Vocabulary: {node["candidate_vocabulary_id"]}', FS_XS, TEXT_LIGHT))
+        parts.append(
+            text(
+                x + 10, next_y, f"Vocabulary: {node['candidate_vocabulary_id']}", FS_XS, TEXT_LIGHT
+            )
+        )
 
     # ── Stage 1 -> 2: Identity hop (or dead end if no mapping at all) ──
-    std_order: List[int] = []
-    std_indices: Dict[int, List[int]] = {}
+    std_order: list[int] = []
+    std_indices: dict[int, list[int]] = {}
     for i, node in enumerate(nodes):
         sid = node["standard_id"]
         if sid is None:
             x1, y1 = col_xs[0] + COL_W, node_cy[i]
             parts.append(arrow(x1, y1, x1 + 26, y1, FAIL_BORDER, dashed=True))
-            parts.append(text(x1 + 32, y1 + 4, "✗ no Identity mapping found", FS_XS, FAIL_BORDER, bold=True))
+            parts.append(
+                text(x1 + 32, y1 + 4, "✗ no Identity mapping found", FS_XS, FAIL_BORDER, bold=True)
+            )
             continue
         std_indices.setdefault(sid, [])
         if sid not in std_order:
@@ -1369,10 +1656,17 @@ def outcome_colors(passed, winner):
 
     std_cy = {sid: sum(node_cy[i] for i in idxs) / len(idxs) for sid, idxs in std_indices.items()}
     std_passed = {sid: any(nodes[i]["passed"] for i in idxs) for sid, idxs in std_indices.items()}
-    std_winner = {sid: any(nodes[i]["is_winner"] for i in idxs) for sid, idxs in std_indices.items()}
-    std_name = {sid: next(nodes[i]["standard_name"] for i in idxs) for sid, idxs in std_indices.items()}
+    std_winner = {
+        sid: any(nodes[i]["is_winner"] for i in idxs) for sid, idxs in std_indices.items()
+    }
+    std_name = {
+        sid: next(nodes[i]["standard_name"] for i in idxs) for sid, idxs in std_indices.items()
+    }
     std_sep = {
-        sid: min((nodes[i]["separation"] for i in idxs if nodes[i]["separation"] is not None), default=None)
+        sid: min(
+            (nodes[i]["separation"] for i in idxs if nodes[i]["separation"] is not None),
+            default=None,
+        )
         for sid, idxs in std_indices.items()
     }
     std_score = {
@@ -1382,7 +1676,11 @@ def outcome_colors(passed, winner):
     std_edge_label = {}
     for sid, idxs in std_indices.items():
         rep = nodes[idxs[0]]
-        std_edge_label[sid] = "Already standard (no hop)" if rep["already_standard"] else (rep["edge_predicate"] or "Identity mapping")
+        std_edge_label[sid] = (
+            "Already standard (no hop)"
+            if rep["already_standard"]
+            else (rep["edge_predicate"] or "Identity mapping")
+        )
 
     for i, node in enumerate(nodes):
         sid = node["standard_id"]
@@ -1390,16 +1688,32 @@ def outcome_colors(passed, winner):
             continue
         x1, y1 = col_xs[0] + COL_W, node_cy[i]
         x2, y2 = col_xs[1], std_cy[sid]
-        color = WIN_BORDER if node["is_winner"] else (PASS_BORDER if node["passed"] else FAIL_BORDER)
-        parts.append(arrow(x1, y1, x2, y2, color, dashed=not node["passed"], bold=node["is_winner"]))
+        color = (
+            WIN_BORDER if node["is_winner"] else (PASS_BORDER if node["passed"] else FAIL_BORDER)
+        )
+        parts.append(
+            arrow(x1, y1, x2, y2, color, dashed=not node["passed"], bold=node["is_winner"])
+        )
 
     for sid in std_order:
         x, y = col_xs[1], std_cy[sid] - NODE_H / 2
         bg, border = outcome_colors(std_passed[sid], std_winner[sid])
-        parts.append(rect(x, y, COL_W, NODE_H, bg, border, rx=6, sw=2 if std_winner[sid] else 1.5, dashed=not std_passed[sid]))
+        parts.append(
+            rect(
+                x,
+                y,
+                COL_W,
+                NODE_H,
+                bg,
+                border,
+                rx=6,
+                sw=2 if std_winner[sid] else 1.5,
+                dashed=not std_passed[sid],
+            )
+        )
         box_parts, next_y = name_id_box(x, y, std_name[sid], sid)
         parts.extend(box_parts)
-        parts.append(text(x + 10, next_y, f'via: {std_edge_label[sid]}', FS_XS, TEXT_LIGHT))
+        parts.append(text(x + 10, next_y, f"via: {std_edge_label[sid]}", FS_XS, TEXT_LIGHT))
 
     # ── Stage 2 -> 3: hierarchy validation (kept vs. culled) ──
     for sid in std_order:
@@ -1412,10 +1726,25 @@ def outcome_colors(passed, winner):
         color = WIN_BORDER if std_winner[sid] else PASS_BORDER
         parts.append(arrow(x1, y1, x2, y1, color, bold=std_winner[sid]))
         bg, border = outcome_colors(True, std_winner[sid])
-        parts.append(rect(x2, y1 - NODE_H / 2, COL_W, NODE_H, bg, border, rx=6, sw=2 if std_winner[sid] else 1.5))
+        parts.append(
+            rect(
+                x2,
+                y1 - NODE_H / 2,
+                COL_W,
+                NODE_H,
+                bg,
+                border,
+                rx=6,
+                sw=2 if std_winner[sid] else 1.5,
+            )
+        )
         ccx = x2 + COL_W / 2
-        parts.append(text(ccx, y1 - 4, "✓ Passed hierarchy constraint", FS_SM, border, "middle", bold=True))
-        dist_str = f'Distance to Hierarchy Anchor: {std_sep[sid]}' if std_sep[sid] is not None else ""
+        parts.append(
+            text(ccx, y1 - 4, "✓ Passed hierarchy constraint", FS_SM, border, "middle", bold=True)
+        )
+        dist_str = (
+            f"Distance to Hierarchy Anchor: {std_sep[sid]}" if std_sep[sid] is not None else ""
+        )
         parts.append(text(ccx, y1 + 14, dist_str, FS_XS, TEXT_LIGHT, "middle"))
 
     # ── Stage 3 -> 4: scoring (final rank) ──
@@ -1429,15 +1758,26 @@ def outcome_colors(passed, winner):
         parts.append(arrow(x1, y, x2, y, color, bold=std_winner[sid]))
         bg, border = outcome_colors(True, std_winner[sid])
         box_y = y - NODE_H / 2
-        parts.append(rect(x2, box_y, COL_W, NODE_H, bg, border, rx=6, sw=2 if std_winner[sid] else 1.5))
+        parts.append(
+            rect(x2, box_y, COL_W, NODE_H, bg, border, rx=6, sw=2 if std_winner[sid] else 1.5)
+        )
         box_parts, next_y = name_id_box(x2, box_y, std_name[sid], sid)
         parts.extend(box_parts)
-        score_str = f'{std_score[sid]:.3f}' if std_score[sid] is not None else "n/a"
+        score_str = f"{std_score[sid]:.3f}" if std_score[sid] is not None else "n/a"
         if std_winner[sid]:
             status = f"★ WINNER (rank 1), Score: {score_str}"
         else:
             status = f"Score: {score_str}"
-        parts.append(text(x2 + 10, next_y, status, FS_XS, WIN_BORDER if std_winner[sid] else TEXT_LIGHT, bold=std_winner[sid]))
+        parts.append(
+            text(
+                x2 + 10,
+                next_y,
+                status,
+                FS_XS,
+                WIN_BORDER if std_winner[sid] else TEXT_LIGHT,
+                bold=std_winner[sid],
+            )
+        )
 
     # ── Legend: resolver-attribution stripe colors ──
     legend_y = H - BOTTOM_MARGIN - 4
@@ -1446,7 +1786,9 @@ def outcome_colors(passed, winner):
     lx += 58
     for g in ("Exact", "Partial", "Full Text", "Embedding"):
         color = RESOLVER_STRIPE_COLORS[g]
-        parts.append(f'<rect x="{lx}" y="{legend_y-10}" width="14" height="14" rx="3" fill="{color}"/>')
+        parts.append(
+            f'<rect x="{lx}" y="{legend_y - 10}" width="14" height="14" rx="3" fill="{color}"/>'
+        )
         parts.append(text(lx + 20, legend_y + 1, g, FS_XS, TEXT_MID))
         lx += 9 * len(g) + 50
 
@@ -1459,42 +1801,112 @@ def outcome_colors(passed, winner):
 
 # ─── CLI commands ──────────────────────────────────────────────────────────────
 
+
 @app.command()
 def trace(
-    cases_file: Annotated[Optional[str], typer.Option("--cases-file", "-c",
-        help="JSON benchmark cases file. Runs all cases unless --case-ids is given.")] = None,
-    case_ids: Annotated[Optional[List[str]], typer.Option("--case-id", "-i",
-        help="Optional case IDs to run. For multiple IDs, repeat the option (e.g., -i case1 -i case2). Default: None (all cases run)")] = None,
-    query: Annotated[Optional[str], typer.Option("--query", "-q",
-        help="Query text for on-demand evaluation, circumventing the cases file.")] = None,
-    parent_ids: Annotated[Optional[List[int]], typer.Option("--parent-id", "-G",
-        help="Provide parent concept IDs for hierarchy anchoring. For multiple IDs, repeat the option (e.g., -G 443392 -G 413015).")] = None,
-    domains: Annotated[Optional[List[str]], typer.Option("--domain", "-D",
-        help="OMOP domain filter. For multiple domains, repeat the option (e.g., -D Condition -D Procedure).")] = None,
-    vocabularies: Annotated[Optional[List[str]], typer.Option("--vocabulary", "-V",
-        help="Vocabulary filter. For multiple vocabularies, repeat the option (e.g., -V SNOMED -V ICDO3).")] = None,
-    query_expected_concept_id: Annotated[Optional[int], typer.Option("--expected-concept-id", "-e",
-        help="Expected concept ID for on-demand evaluation.")] = None,
-    top_n: Annotated[int, typer.Option("--top-n", "-n",
-        help="Number of top results to include per case.")] = 5,
-    out_dir: Annotated[Optional[str], typer.Option("--out-dir", "-o",
-        help="Base output directory. Writes one file per case to <out-dir>/<safe_model_name>/trace_<case_id>.json. If omitted, prints combined JSON to stdout.")] = None,
-    metric_type: Annotated[str, typer.Option("--metric-type", "-m",
-        help="Distance metric used when embedding was indexed (cosine or l2). Must match the metric registered for the model.")] = "cosine",
-    embedding_model: Annotated[Optional[str], typer.Option("--embedding-model", "-E",
-        help="Embedding model name to use, overriding the one configured in config.toml (e.g. to compare multiple ingested models).")] = None,
-    parent_id_level: Annotated[Optional[int], typer.Option("--parent-id-level", "-L",
-        help="Use case['parent_ids_by_level'][N] as the anchor instead of case['parent_ids'] "
-             "(see enrich_gold_standard.py). Falls back to case['parent_ids'] for cases/files "
-             "without per-level data. Also nests output under parent_id_level_<N>/.")] = None,
+    cases_file: Annotated[
+        str | None,
+        typer.Option(
+            "--cases-file",
+            "-c",
+            help="JSON benchmark cases file. Runs all cases unless --case-ids is given.",
+        ),
+    ] = None,
+    case_ids: Annotated[
+        list[str] | None,
+        typer.Option(
+            "--case-id",
+            "-i",
+            help="Optional case IDs to run. For multiple IDs, repeat the option (e.g., -i case1 -i case2). Default: None (all cases run)",
+        ),
+    ] = None,
+    query: Annotated[
+        str | None,
+        typer.Option(
+            "--query",
+            "-q",
+            help="Query text for on-demand evaluation, circumventing the cases file.",
+        ),
+    ] = None,
+    parent_ids: Annotated[
+        list[int] | None,
+        typer.Option(
+            "--parent-id",
+            "-G",
+            help="Provide parent concept IDs for hierarchy anchoring. For multiple IDs, repeat the option (e.g., -G 443392 -G 413015).",
+        ),
+    ] = None,
+    domains: Annotated[
+        list[str] | None,
+        typer.Option(
+            "--domain",
+            "-D",
+            help="OMOP domain filter. For multiple domains, repeat the option (e.g., -D Condition -D Procedure).",
+        ),
+    ] = None,
+    vocabularies: Annotated[
+        list[str] | None,
+        typer.Option(
+            "--vocabulary",
+            "-V",
+            help="Vocabulary filter. For multiple vocabularies, repeat the option (e.g., -V SNOMED -V ICDO3).",
+        ),
+    ] = None,
+    query_expected_concept_id: Annotated[
+        int | None,
+        typer.Option(
+            "--expected-concept-id", "-e", help="Expected concept ID for on-demand evaluation."
+        ),
+    ] = None,
+    top_n: Annotated[
+        int, typer.Option("--top-n", "-n", help="Number of top results to include per case.")
+    ] = 5,
+    out_dir: Annotated[
+        str | None,
+        typer.Option(
+            "--out-dir",
+            "-o",
+            help="Base output directory. Writes one file per case to <out-dir>/<safe_model_name>/trace_<case_id>.json. If omitted, prints combined JSON to stdout.",
+        ),
+    ] = None,
+    metric_type: Annotated[
+        str,
+        typer.Option(
+            "--metric-type",
+            "-m",
+            help="Distance metric used when embedding was indexed (cosine or l2). Must match the metric registered for the model.",
+        ),
+    ] = "cosine",
+    embedding_model: Annotated[
+        str | None,
+        typer.Option(
+            "--embedding-model",
+            "-E",
+            help="Embedding model name to use, overriding the one configured in config.toml (e.g. to compare multiple ingested models).",
+        ),
+    ] = None,
+    parent_id_level: Annotated[
+        int | None,
+        typer.Option(
+            "--parent-id-level",
+            "-L",
+            help="Use case['parent_ids_by_level'][N] as the anchor instead of case['parent_ids'] "
+            "(see enrich_gold_standard.py). Falls back to case['parent_ids'] for cases/files "
+            "without per-level data. Also nests output under parent_id_level_<N>/.",
+        ),
+    ] = None,
 ):
     """Trace grounding cases through every resolver stage. Outputs {\"cases\": [...]}."""
     # Build the list of cases to run
-    cases_to_run: List[Dict] = []
-    
+    cases_to_run: list[dict] = []
+
     if cases_file:
         payload = json.loads(Path(cases_file).read_text())
-        rows = payload if isinstance(payload, list) else [r for bucket in payload.values() for r in bucket]
+        rows = (
+            payload
+            if isinstance(payload, list)
+            else [r for bucket in payload.values() for r in bucket]
+        )
         if case_ids:
             case_ids_str = {str(cid) for cid in case_ids}
             rows = [r for r in rows if str(r.get("id")) in case_ids_str]
@@ -1506,12 +1918,14 @@ def trace(
         if not parent_ids:
             console.print("--parent-ids required with --query.")
             raise typer.Exit(1)
-        cases_to_run = [{
-            "id": "on-demand",
-            "text": query,
-            "expected_concept_id": query_expected_concept_id,
-            "expected_concept_name": None,
-        }]
+        cases_to_run = [
+            {
+                "id": "on-demand",
+                "text": query,
+                "expected_concept_id": query_expected_concept_id,
+                "expected_concept_name": None,
+            }
+        ]
     else:
         console.print("Provide --cases-file or --query.")
         raise typer.Exit(1)
@@ -1522,6 +1936,7 @@ def trace(
 
     console.print("Building knowledge graph…")
     from omop_emb.config import parse_metric_type
+
     kg = _build_kg(metric_type=parse_metric_type(metric_type), embedding_model=embedding_model)
     used_embedding_model = (
         kg.embedding_configuration.model_name if kg.embedding_configuration else None
@@ -1541,13 +1956,17 @@ def trace(
             case_id = case.get("id")
             case_query = case.get("text")
             case_domains = domains or ([case.get("domain")] if case.get("domain") else None)
-            case_vocab = vocabularies or ([case.get("vocabulary")] if case.get("vocabulary") else None)
+            case_vocab = vocabularies or (
+                [case.get("vocabulary")] if case.get("vocabulary") else None
+            )
             if parent_ids:
                 case_parent_ids = parent_ids
             elif parent_id_level is not None:
                 by_level = case.get("parent_ids_by_level")
                 if by_level is None:
-                    console.print(f"Case '{case_id}': no parent_ids_by_level, falling back to parent_ids.")
+                    console.print(
+                        f"Case '{case_id}': no parent_ids_by_level, falling back to parent_ids."
+                    )
                     case_parent_ids = case.get("parent_ids")
                 else:
                     case_parent_ids = by_level.get(str(parent_id_level))
@@ -1570,8 +1989,12 @@ def trace(
                 continue
 
             search_constraint = ConceptFilter(
-                domains=tuple([str(c) for c in case_domains]) if isinstance(case_domains, list) else None,
-                vocabularies=tuple([str(v) for v in case_vocab]) if isinstance(case_vocab, list) else None,
+                domains=tuple([str(c) for c in case_domains])
+                if isinstance(case_domains, list)
+                else None,
+                vocabularies=tuple([str(v) for v in case_vocab])
+                if isinstance(case_vocab, list)
+                else None,
                 require_standard=False,
             )
 
@@ -1623,20 +2046,45 @@ def trace(
 
 @app.command(name="pipeline-svg")
 def pipeline_svg(
-    trace_dir: Annotated[str, typer.Option("--trace-dir", "-t",
-        help="Base output directory passed to 'trace --out-dir'. The model-specific "
-             "subdirectory is resolved the same way 'trace' does, via --embedding-model "
-             "or config.toml.")],
-    embedding_model: Annotated[Optional[str], typer.Option("--embedding-model", "-E",
-        help="Embedding model name used to resolve the subdirectory, overriding "
-             "config.toml (same semantics as 'trace --embedding-model').")] = None,
-    case_id: Annotated[Optional[str], typer.Option("--case-id", "-i",
-        help="Case ID to render. Omitted, empty, or 'all' renders every case found.")] = None,
-    title: Annotated[str, typer.Option("--title",
-        help="Figure title shown at the top.")] = "omop-graph Concept Grounding",
-    parent_id_level: Annotated[Optional[int], typer.Option("--parent-id-level", "-L",
-        help="Same --parent-id-level passed to 'trace' -- resolves the matching "
-             "parent_id_level_<N>/ subdirectory.")] = None,
+    trace_dir: Annotated[
+        str,
+        typer.Option(
+            "--trace-dir",
+            "-t",
+            help="Base output directory passed to 'trace --out-dir'. The model-specific "
+            "subdirectory is resolved the same way 'trace' does, via --embedding-model "
+            "or config.toml.",
+        ),
+    ],
+    embedding_model: Annotated[
+        str | None,
+        typer.Option(
+            "--embedding-model",
+            "-E",
+            help="Embedding model name used to resolve the subdirectory, overriding "
+            "config.toml (same semantics as 'trace --embedding-model').",
+        ),
+    ] = None,
+    case_id: Annotated[
+        str | None,
+        typer.Option(
+            "--case-id",
+            "-i",
+            help="Case ID to render. Omitted, empty, or 'all' renders every case found.",
+        ),
+    ] = None,
+    title: Annotated[
+        str, typer.Option("--title", help="Figure title shown at the top.")
+    ] = "omop-graph Concept Grounding",
+    parent_id_level: Annotated[
+        int | None,
+        typer.Option(
+            "--parent-id-level",
+            "-L",
+            help="Same --parent-id-level passed to 'trace' -- resolves the matching "
+            "parent_id_level_<N>/ subdirectory.",
+        ),
+    ] = None,
 ):
     """Generate resolver-pipeline flowchart SVG(s) from trace JSON files written by the trace
     command. Writes them to the plots/pipeline/ subdirectory of the same directory as the traces."""
@@ -1649,7 +2097,7 @@ def pipeline_svg(
     if not json_files:
         typer.echo(f"No JSON trace files found in {trace_dir_pl}.", err=True)
         raise typer.Exit(1)
-    cases: List[Dict] = []
+    cases: list[dict] = []
     for json_file in json_files:
         payload = json.loads(json_file.read_text(encoding="utf-8"))
         cases.extend(payload.get("cases", [payload]))
@@ -1674,23 +2122,57 @@ def pipeline_svg(
 
 @app.command(name="panel-svg")
 def panel_svg(
-    trace_dir: Annotated[str, typer.Option("--trace-dir", "-t",
-        help="Base output directory passed to 'trace --out-dir'. The model-specific "
-             "subdirectory is resolved the same way 'trace' does, via --embedding-model "
-             "or config.toml.")],
-    embedding_model: Annotated[Optional[str], typer.Option("--embedding-model", "-E",
-        help="Embedding model name used to resolve the subdirectory, overriding "
-             "config.toml (same semantics as 'trace --embedding-model').")] = None,
-    case_id: Annotated[Optional[str], typer.Option("--case-id", "-i",
-        help="Case ID to render. Omitted, empty, or 'all' renders every case found.")] = None,
-    concept_id: Annotated[Optional[int], typer.Option("--concept-id",
-        help="Override the concept whose outgoing edges Panel A classifies. Defaults to the "
-             "case's winning (target) concept. Only meaningful when rendering a single case.")] = None,
-    title: Annotated[str, typer.Option("--title",
-        help="Figure title (kept for CLI parity with pipeline-svg; not currently rendered).")] = "omop-graph Graph Traversal",
-    parent_id_level: Annotated[Optional[int], typer.Option("--parent-id-level", "-L",
-        help="Same --parent-id-level passed to 'trace' -- resolves the matching "
-             "parent_id_level_<N>/ subdirectory.")] = None,
+    trace_dir: Annotated[
+        str,
+        typer.Option(
+            "--trace-dir",
+            "-t",
+            help="Base output directory passed to 'trace --out-dir'. The model-specific "
+            "subdirectory is resolved the same way 'trace' does, via --embedding-model "
+            "or config.toml.",
+        ),
+    ],
+    embedding_model: Annotated[
+        str | None,
+        typer.Option(
+            "--embedding-model",
+            "-E",
+            help="Embedding model name used to resolve the subdirectory, overriding "
+            "config.toml (same semantics as 'trace --embedding-model').",
+        ),
+    ] = None,
+    case_id: Annotated[
+        str | None,
+        typer.Option(
+            "--case-id",
+            "-i",
+            help="Case ID to render. Omitted, empty, or 'all' renders every case found.",
+        ),
+    ] = None,
+    concept_id: Annotated[
+        int | None,
+        typer.Option(
+            "--concept-id",
+            help="Override the concept whose outgoing edges Panel A classifies. Defaults to the "
+            "case's winning (target) concept. Only meaningful when rendering a single case.",
+        ),
+    ] = None,
+    title: Annotated[
+        str,
+        typer.Option(
+            "--title",
+            help="Figure title (kept for CLI parity with pipeline-svg; not currently rendered).",
+        ),
+    ] = "omop-graph Graph Traversal",
+    parent_id_level: Annotated[
+        int | None,
+        typer.Option(
+            "--parent-id-level",
+            "-L",
+            help="Same --parent-id-level passed to 'trace' -- resolves the matching "
+            "parent_id_level_<N>/ subdirectory.",
+        ),
+    ] = None,
 ):
     """Generate the Panel A/B dashboard SVG(s): Panel A shows a concept's outgoing edges grouped
     by relationship class (Identity = walkable for grounding vs. everything else = excluded by
@@ -1706,7 +2188,7 @@ def panel_svg(
     if not json_files:
         typer.echo(f"No JSON trace files found in {trace_dir_pl}.", err=True)
         raise typer.Exit(1)
-    cases: List[Dict] = []
+    cases: list[dict] = []
     for json_file in json_files:
         payload = json.loads(json_file.read_text(encoding="utf-8"))
         cases.extend(payload.get("cases", [payload]))
@@ -1720,19 +2202,28 @@ def panel_svg(
         selected = cases
 
     kg = KnowledgeGraph(cdm_session_factory())
-    rel_class_cache: Dict[int, Dict] = {}
+    rel_class_cache: dict[int, dict] = {}
 
     out_dir = trace_dir_pl / "plots" / "panel"
     out_dir.mkdir(parents=True, exist_ok=True)
     for trace_data in selected:
         cid = trace_data.get("case_id") or "ad-hoc"
-        target_result = next((r for r in trace_data.get("top_n_results", []) if r.get("is_target")), None)
-        panel_a_concept_id = concept_id or (target_result.get("concept_id") if target_result else None)
+        target_result = next(
+            (r for r in trace_data.get("top_n_results", []) if r.get("is_target")), None
+        )
+        panel_a_concept_id = concept_id or (
+            target_result.get("concept_id") if target_result else None
+        )
         if panel_a_concept_id is None:
-            typer.echo(f"Case '{cid}': no winning target concept to classify edges for, skipping.", err=True)
+            typer.echo(
+                f"Case '{cid}': no winning target concept to classify edges for, skipping.",
+                err=True,
+            )
             continue
         if panel_a_concept_id not in rel_class_cache:
-            rel_class_cache[panel_a_concept_id] = _relationship_classification(kg, panel_a_concept_id)
+            rel_class_cache[panel_a_concept_id] = _relationship_classification(
+                kg, panel_a_concept_id
+            )
         svg_content = _panel_svg(rel_class_cache[panel_a_concept_id], trace_data, title)
         out_file = out_dir / f"panel_{cid}.svg"
         out_file.write_text(svg_content, encoding="utf-8")
@@ -1741,18 +2232,42 @@ def panel_svg(
 
 @app.command(name="graph-svg")
 def graph_svg(
-    trace_dir: Annotated[str, typer.Option("--trace-dir", "-t",
-        help="Base output directory passed to 'trace --out-dir'. The model-specific "
-             "subdirectory is resolved the same way 'trace' does, via --embedding-model "
-             "or config.toml.")],
-    embedding_model: Annotated[Optional[str], typer.Option("--embedding-model", "-E",
-        help="Embedding model name used to resolve the subdirectory, overriding "
-             "config.toml (same semantics as 'trace --embedding-model').")] = None,
-    case_id: Annotated[Optional[str], typer.Option("--case-id", "-i",
-        help="Case ID to render. Omitted, empty, or 'all' renders every case found.")] = None,
-    parent_id_level: Annotated[Optional[int], typer.Option("--parent-id-level", "-L",
-        help="Same --parent-id-level passed to 'trace' -- resolves the matching "
-             "parent_id_level_<N>/ subdirectory.")] = None,
+    trace_dir: Annotated[
+        str,
+        typer.Option(
+            "--trace-dir",
+            "-t",
+            help="Base output directory passed to 'trace --out-dir'. The model-specific "
+            "subdirectory is resolved the same way 'trace' does, via --embedding-model "
+            "or config.toml.",
+        ),
+    ],
+    embedding_model: Annotated[
+        str | None,
+        typer.Option(
+            "--embedding-model",
+            "-E",
+            help="Embedding model name used to resolve the subdirectory, overriding "
+            "config.toml (same semantics as 'trace --embedding-model').",
+        ),
+    ] = None,
+    case_id: Annotated[
+        str | None,
+        typer.Option(
+            "--case-id",
+            "-i",
+            help="Case ID to render. Omitted, empty, or 'all' renders every case found.",
+        ),
+    ] = None,
+    parent_id_level: Annotated[
+        int | None,
+        typer.Option(
+            "--parent-id-level",
+            "-L",
+            help="Same --parent-id-level passed to 'trace' -- resolves the matching "
+            "parent_id_level_<N>/ subdirectory.",
+        ),
+    ] = None,
 ):
     """Generate the 4-stage funnel diagram SVG(s): candidates (stacked vertically, left, each
     striped by which resolver(s) found it) -> standard concepts (via a real, live-looked-up
@@ -1769,7 +2284,7 @@ def graph_svg(
     if not json_files:
         typer.echo(f"No JSON trace files found in {trace_dir_pl}.", err=True)
         raise typer.Exit(1)
-    cases: List[Dict] = []
+    cases: list[dict] = []
     for json_file in json_files:
         payload = json.loads(json_file.read_text(encoding="utf-8"))
         cases.extend(payload.get("cases", [payload]))
@@ -1790,7 +2305,10 @@ def graph_svg(
         cid = trace_data.get("case_id") or "ad-hoc"
         candidates = _select_funnel_candidates(trace_data)
         if not candidates:
-            typer.echo(f"Case '{cid}': no resolver hits to build a funnel diagram from, skipping.", err=True)
+            typer.echo(
+                f"Case '{cid}': no resolver hits to build a funnel diagram from, skipping.",
+                err=True,
+            )
             continue
         nodes = _trim_funnel_nodes(_build_funnel_nodes(kg, trace_data, candidates))
         anchor_concepts = _resolve_anchor_concepts(kg, trace_data)
diff --git a/scripts/system_information.py b/scripts/system_information.py
index a212275..96534b4 100644
--- a/scripts/system_information.py
+++ b/scripts/system_information.py
@@ -1,22 +1,23 @@
 import platform
 import sys
 
+
 def get_version(package_name):
     try:
         module = __import__(package_name)
-        return getattr(module, '__version__', 'unknown')
+        return getattr(module, "__version__", "unknown")
     except ImportError:
-        return 'not installed'
+        return "not installed"
 
 
-print('--- System Information ---')
-print(f'Platform:   {platform.platform()}')
-print(f'Python:     {sys.version}')
-print(f'omop-graph:   {get_version("omop_graph")}')
-print(f'omop-emb:   {get_version("omop_emb")}')
-print(f'pgvector:   {get_version("pgvector")}')
-print(f'FAISS CPU:    {get_version("faiss-cpu")}')
-print(f'FAISS GPU:    {get_version("faiss-gpu")}')
-print(f'NumPy:      {get_version("numpy")}')
-print(f'SQLAlchemy: {get_version("sqlalchemy")}')
-print('--------------------------')
\ No newline at end of file
+print("--- System Information ---")
+print(f"Platform:   {platform.platform()}")
+print(f"Python:     {sys.version}")
+print(f"omop-graph:   {get_version('omop_graph')}")
+print(f"omop-emb:   {get_version('omop_emb')}")
+print(f"pgvector:   {get_version('pgvector')}")
+print(f"FAISS CPU:    {get_version('faiss-cpu')}")
+print(f"FAISS GPU:    {get_version('faiss-gpu')}")
+print(f"NumPy:      {get_version('numpy')}")
+print(f"SQLAlchemy: {get_version('sqlalchemy')}")
+print("--------------------------")
diff --git a/src/omop_graph/__init__.py b/src/omop_graph/__init__.py
index 86da8dd..e6609cc 100644
--- a/src/omop_graph/__init__.py
+++ b/src/omop_graph/__init__.py
@@ -1,17 +1,17 @@
 from .api import (
-    KnowledgeGraph,
     GraphPath,
-    Subgraph,
     GraphTrace,
+    KnowledgeGraph,
     PathExplanation,
     PathProfile,
+    Subgraph,
 )
 
 __all__ = [
-    "KnowledgeGraph",
     "GraphPath",
-    "Subgraph",
     "GraphTrace",
+    "KnowledgeGraph",
     "PathExplanation",
     "PathProfile",
+    "Subgraph",
 ]
diff --git a/src/omop_graph/api.py b/src/omop_graph/api.py
index cb5b066..1b0470c 100644
--- a/src/omop_graph/api.py
+++ b/src/omop_graph/api.py
@@ -11,26 +11,26 @@
 
 from omop_graph.graph.kg import KnowledgeGraph
 from omop_graph.graph.paths import GraphPath, PathExplanation, PathProfile
-from omop_graph.graph.traverse import Subgraph, GraphTrace
+from omop_graph.graph.traverse import GraphTrace, Subgraph
+
+from .reasoning.grounding import (
+    GroundingConstraints,
+)
 from .reasoning.resolvers import (
     CandidateResolver,
     ExactLabelResolver,
     ResolverPipeline,
 )
 
-from .reasoning.grounding import (
-    GroundingConstraints,
-)
-
 __all__ = [
-    "KnowledgeGraph",
-    "GraphPath",
-    "Subgraph",
-    "GraphTrace",
     "CandidateResolver",
     "ExactLabelResolver",
+    "GraphPath",
+    "GraphTrace",
     "GroundingConstraints",
-    "ResolverPipeline",
+    "KnowledgeGraph",
     "PathExplanation",
     "PathProfile",
+    "ResolverPipeline",
+    "Subgraph",
 ]
diff --git a/src/omop_graph/cli.py b/src/omop_graph/cli.py
index dff286e..bec051a 100644
--- a/src/omop_graph/cli.py
+++ b/src/omop_graph/cli.py
@@ -2,27 +2,25 @@
 import tempfile
 from importlib import resources
 from pathlib import Path
-from typing import Annotated, Optional, cast
+from typing import Annotated
 
 import pandas as pd
 import sqlalchemy as sa
-import typer
 import sqlalchemy.orm as so
-
+import typer
 from oa_configurator import ResolvedCDMDatabase, open_connection
-
+from omop_alchemy.cdm.model.vocabulary.relationship import Relationship
 from orm_loader.backends import STAGING_SCHEMA, resolve_backend
 from orm_loader.helpers.metadata import Base
-from omop_alchemy.cdm.model.vocabulary.relationship import Relationship
 
+from omop_graph.cli_utils import populate_test_data
 from omop_graph.config import OmopGraphConfig
-from omop_graph.db.session import resolve_cdm_database, open_cdm_sessions
+from omop_graph.db.session import open_cdm_sessions, resolve_cdm_database
 from omop_graph.extensions.omop_alchemy import (
     RelationshipClass,
     RelationshipMapping,
     create_extension_tables,
 )
-from omop_graph.cli_utils import populate_test_data
 
 app = typer.Typer()
 logger = logging.getLogger(__name__)
@@ -77,9 +75,8 @@ def populate_with_test_data():
             f"Refusing to populate {resolved.name!r} with synthetic test data: "
             "both its primary and vocab connections must be test_only."
         )
-    with open_cdm_sessions(resolved) as cdm_sessions:
-        with cdm_sessions() as session:
-            populate_test_data(session)
+    with open_cdm_sessions(resolved) as cdm_sessions, cdm_sessions() as session:
+        populate_test_data(session)
 
 
 def packaged_predicate_csv_dir() -> Path:
@@ -94,7 +91,7 @@ def packaged_predicate_csv_dir() -> Path:
 
 
 def relationship_classification(
-    pred_class_dir: Optional[str] = None,
+    pred_class_dir: str | None = None,
     *,
     resolved: ResolvedCDMDatabase | None = None,
 ) -> None:
@@ -116,9 +113,7 @@ def relationship_classification(
     with open_cdm_sessions(resolved) as cdm_sessions:
         with cdm_sessions() as session:
             engine = session.get_bind(RelationshipMapping).engine
-            relationship_ids = set(
-                session.scalars(sa.select(Relationship.relationship_id)).all()
-            )
+            relationship_ids = set(session.scalars(sa.select(Relationship.relationship_id)).all())
         _load_relationship_classification(
             pred_class_dir,
             resolved=resolved,
@@ -128,29 +123,23 @@ def relationship_classification(
 
 
 def _load_relationship_classification(
-    pred_class_dir: Optional[str],
+    pred_class_dir: str | None,
     *,
     resolved: ResolvedCDMDatabase,
     engine: sa.Engine,
     relationship_ids: set[str],
 ) -> None:
     """Body of :func:`relationship_classification`, on *engine*, which hosts the extension tables."""
-    pred_class_dir_pl = (
-        Path(pred_class_dir) if pred_class_dir else packaged_predicate_csv_dir()
-    )
+    pred_class_dir_pl = Path(pred_class_dir) if pred_class_dir else packaged_predicate_csv_dir()
     if not pred_class_dir_pl.is_dir():
         raise NotADirectoryError(f"{pred_class_dir_pl} is not a valid directory.")
 
     pred_mapping_file = pred_class_dir_pl / "predicate_mapping.csv"
     if not pred_mapping_file.is_file():
-        raise FileNotFoundError(
-            f"`predicate_mapping.csv` not found in {pred_class_dir_pl}"
-        )
+        raise FileNotFoundError(f"`predicate_mapping.csv` not found in {pred_class_dir_pl}")
     pred_class_file = pred_class_dir_pl / "predicate_classification.csv"
     if not pred_class_file.is_file():
-        raise FileNotFoundError(
-            f"`predicate_classification.csv` not found in {pred_class_dir_pl}"
-        )
+        raise FileNotFoundError(f"`predicate_classification.csv` not found in {pred_class_dir_pl}")
 
     df_class = pd.read_csv(pred_class_file)
     df_mapping = pd.read_csv(pred_mapping_file)
@@ -163,11 +152,11 @@ def _load_relationship_classification(
     check = df_rel_cls.groupby(["predicate_kind", "predicate_subkind"])[
         ["description", "semantics", "inference"]
     ].nunique(dropna=True)
-    violations = cast(pd.DataFrame, check[(check > 1).any(axis=1)])
+    violations = check[(check > 1).any(axis=1)]
     if not violations.empty:
-        conflicting_data = cast(pd.DataFrame, df_rel_cls[
+        conflicting_data = df_rel_cls[
             df_rel_cls["predicate_subkind"].isin(violations.index.tolist())
-        ]).sort_values("predicate_subkind")
+        ].sort_values("predicate_subkind")
         logger.error(
             f"Validation Failed! {len(violations)} predicate_subkinds have conflicting definitions: {conflicting_data}"
         )
@@ -186,13 +175,9 @@ def _load_relationship_classification(
     )
     df_rel_mapping = df_rel_mapping[
         ["relationship_id", "predicate_kind", "predicate_subkind"]
-    ].dropna(subset=["predicate_kind", "predicate_subkind"], how="all")  # type: ignore[call-overload]
-    df_rel_mapping = _filter_unknown_relationship_ids(
-        df_rel_mapping, relationship_ids
-    )
-    invalid_mask = (
-        df_rel_mapping[["predicate_kind", "predicate_subkind"]].isna().any(axis=1)
-    )
+    ].dropna(subset=["predicate_kind", "predicate_subkind"], how="all")
+    df_rel_mapping = _filter_unknown_relationship_ids(df_rel_mapping, relationship_ids)
+    invalid_mask = df_rel_mapping[["predicate_kind", "predicate_subkind"]].isna().any(axis=1)
     dropped_ids = df_rel_mapping.loc[invalid_mask, "relationship_id"].unique().tolist()
 
     if dropped_ids:
@@ -232,7 +217,7 @@ def _load_relationship_classification(
         RelationshipClass.__table__,
     ]
     with open_connection(engine) as connection:
-        Base.metadata.drop_all(bind=connection, tables=tables_to_drop, checkfirst=True)  # type: ignore
+        Base.metadata.drop_all(bind=connection, tables=tables_to_drop, checkfirst=True)  # ty: ignore[invalid-argument-type]
         create_extension_tables(connection, resolved=resolved)
 
     with tempfile.TemporaryDirectory() as tmp_dir, so.Session(engine) as session:
@@ -265,7 +250,7 @@ def _load_relationship_classification(
 @app.command(name="relationship-classification")
 def relationship_classification_cmd(
     pred_class_dir: Annotated[
-        Optional[str],
+        str | None,
         typer.Option(
             help=(
                 "Path to the directory containing `predicate_classification.csv` "
diff --git a/src/omop_graph/cli_utils/cli_add_test_data.py b/src/omop_graph/cli_utils/cli_add_test_data.py
index 9af3e74..4eb8728 100644
--- a/src/omop_graph/cli_utils/cli_add_test_data.py
+++ b/src/omop_graph/cli_utils/cli_add_test_data.py
@@ -1,27 +1,26 @@
-from random import randint, choice
-import numpy as np
 from datetime import date, timedelta
-import pandas as pd
+from random import choice, randint
 
+import numpy as np
+import pandas as pd
 import sqlalchemy as sa
-from sqlalchemy.orm import Session
-
-from omop_alchemy.cdm.model.structural.episode import Episode
-from omop_alchemy.cdm.model.structural.episode_event import Episode_Event
-from omop_alchemy.cdm.model.clinical import Observation_Period
-from omop_alchemy.cdm.model.health_system import (
-    Location,
-    Care_Site,
-    Provider,
-    Visit_Occurrence,
-)
 from omop_alchemy.cdm.model.clinical import (
-    Person,
     Condition_Occurrence,
     Death,
     Measurement,
+    Observation_Period,
+    Person,
 )
+from omop_alchemy.cdm.model.health_system import (
+    Care_Site,
+    Location,
+    Provider,
+    Visit_Occurrence,
+)
+from omop_alchemy.cdm.model.structural.episode import Episode
+from omop_alchemy.cdm.model.structural.episode_event import Episode_Event
 from omop_alchemy.cdm.model.vocabulary import Concept, Concept_Ancestor
+from sqlalchemy.orm import Session
 
 
 def populate_reference_data(
@@ -221,9 +220,7 @@ def populate_conditions_and_modifiers(
             episode_concept_id=32533,  # Episode of care
             episode_object_concept_id=condition.condition_concept_id,
             episode_start_date=condition.condition_start_date,
-            episode_end_date=(
-                death.death_date if death else obs.observation_period_end_date
-            ),
+            episode_end_date=(death.death_date if death else obs.observation_period_end_date),
             episode_type_concept_id=choice(avail_types),  # EHR / registry / derived
         )
         episodes.append(episode)
@@ -288,34 +285,22 @@ def populate_test_data(session):
                         "Type Concept",
                     ]
                 ),
-                sa.and_(
-                    Concept.domain_id == "Condition", Concept.vocabulary_id == "ICDO3"
-                ),
+                sa.and_(Concept.domain_id == "Condition", Concept.vocabulary_id == "ICDO3"),
             )
         )
     )
 
-    avail_gender = list(
-        concept_by_domain[concept_by_domain.domain_id == "Gender"].concept_id
-    )
-    avail_ethnicity = list(
-        concept_by_domain[concept_by_domain.domain_id == "Ethnicity"].concept_id
-    )
-    avail_race = list(
-        concept_by_domain[concept_by_domain.domain_id == "Race"].concept_id
-    )
+    avail_gender = list(concept_by_domain[concept_by_domain.domain_id == "Gender"].concept_id)
+    avail_ethnicity = list(concept_by_domain[concept_by_domain.domain_id == "Ethnicity"].concept_id)
+    avail_race = list(concept_by_domain[concept_by_domain.domain_id == "Race"].concept_id)
     avail_place_of_service = list(
         concept_by_domain[concept_by_domain.domain_id == "Visit"].concept_id
     )
     avail_country = list(
         concept_by_domain[concept_by_domain.concept_class_id == "Location"].concept_id
     )
-    avail_provider = list(
-        concept_by_domain[concept_by_domain.domain_id == "Provider"].concept_id
-    )
-    avail_types = list(
-        concept_by_domain[concept_by_domain.domain_id == "Type Concept"].concept_id
-    )
+    avail_provider = list(concept_by_domain[concept_by_domain.domain_id == "Provider"].concept_id)
+    avail_types = list(concept_by_domain[concept_by_domain.domain_id == "Type Concept"].concept_id)
     cancers = list(
         concept_by_domain[
             (concept_by_domain.domain_id == "Condition")
@@ -337,9 +322,7 @@ def populate_test_data(session):
     staging_sets = {}
 
     for axis in ["T", "N", "M", "Stage"]:
-        parents = list(
-            staging_parents[staging_parents.concept_name.str.contains(axis)].concept_id
-        )
+        parents = list(staging_parents[staging_parents.concept_name.str.contains(axis)].concept_id)
         s = pd.DataFrame(
             session.query(*Concept.__table__.columns)
             .join(
diff --git a/src/omop_graph/config.py b/src/omop_graph/config.py
index e45eb39..cab7db8 100644
--- a/src/omop_graph/config.py
+++ b/src/omop_graph/config.py
@@ -4,8 +4,14 @@
 
 from typing import Annotated, ClassVar, Final
 
+from oa_configurator import (
+    CDMDatabaseConfig,
+    ModelConfig,
+    PackageConfigBase,
+    RefTo,
+    VectorStoreConfig,
+)
 from pydantic import Field
-from oa_configurator import CDMDatabaseConfig, ModelConfig, PackageConfigBase, RefTo, VectorStoreConfig
 
 TOOL_NAME: Final[str] = "omop_graph"
 
@@ -37,24 +43,18 @@ class OmopGraphConfig(PackageConfigBase):
     )
 
     cdm_db: Annotated[str, RefTo(CDMDatabaseConfig)] = "cdm_db"
-    test_cdm_db_pg: Annotated[
-        str | None, RefTo(CDMDatabaseConfig, is_test=True)
-    ] = Field(
+    test_cdm_db_pg: Annotated[str | None, RefTo(CDMDatabaseConfig, is_test=True)] = Field(
         default=None,
         description="Real PostgreSQL test CDM database, for Postgres-only integration testing.",
     )
-    test_cdm_db_sqlite: Annotated[
-        str | None, RefTo(CDMDatabaseConfig, is_test=True)
-    ] = Field(
+    test_cdm_db_sqlite: Annotated[str | None, RefTo(CDMDatabaseConfig, is_test=True)] = Field(
         default=None,
         description=(
             "Disposable SQLite test database; left unconfigured by design "
             "(isolated_test_database(..., dialect='sqlite') provisions one automatically)."
         ),
     )
-    test_cdm_db_duckdb: Annotated[
-        str | None, RefTo(CDMDatabaseConfig, is_test=True)
-    ] = Field(
+    test_cdm_db_duckdb: Annotated[str | None, RefTo(CDMDatabaseConfig, is_test=True)] = Field(
         default=None,
         description=(
             "Disposable DuckDB test CDM database; left unconfigured by design "
diff --git a/src/omop_graph/db/session.py b/src/omop_graph/db/session.py
index 826f088..9135c03 100644
--- a/src/omop_graph/db/session.py
+++ b/src/omop_graph/db/session.py
@@ -2,20 +2,20 @@
 
 from __future__ import annotations
 
-from typing import Optional
 from collections.abc import Generator
 from contextlib import contextmanager
+
 import sqlalchemy.orm as so
-from sqlalchemy.engine import Engine
 from oa_configurator import ResolvedCDMDatabase, Resolver
 from omop_alchemy.config import create_cdm_engines
 from omop_alchemy.cross_database import CDMSession, cdm_sessionmaker
+from sqlalchemy.engine import Engine
 from sqlalchemy.orm import sessionmaker
 
 from omop_graph.config import OmopGraphConfig
 
 
-def resolve_cdm_database(name: Optional[str] = None) -> ResolvedCDMDatabase:
+def resolve_cdm_database(name: str | None = None) -> ResolvedCDMDatabase:
     """Resolve a CDM database from the active oa-configurator config.
 
     Parameters
@@ -44,7 +44,7 @@ def resolve_cdm_database(name: Optional[str] = None) -> ResolvedCDMDatabase:
 
 
 def cdm_session_factory(
-    name: Optional[str] = None, *, resolved: ResolvedCDMDatabase | None = None
+    name: str | None = None, *, resolved: ResolvedCDMDatabase | None = None
 ) -> sessionmaker[CDMSession]:
     """Read-only routed session factory on a CDM database from the active config.
 
@@ -65,9 +65,7 @@ def _create_cdm_sessions(
     resolved: ResolvedCDMDatabase, *, register_claims: bool
 ) -> tuple[sessionmaker[CDMSession], Engine, Engine]:
     """Build routed sessions and their owned engine pair through omop-alchemy."""
-    primary, vocab = create_cdm_engines(
-        resolved, register_claims=register_claims
-    )
+    primary, vocab = create_cdm_engines(resolved, register_claims=register_claims)
     return cdm_sessionmaker(resolved, primary=primary, vocab=vocab), primary, vocab
 
 
@@ -87,9 +85,7 @@ def open_cdm_sessions(
     ------
     sqlalchemy.orm.sessionmaker[CDMSession]
     """
-    cdm_sessions, primary, vocab = _create_cdm_sessions(
-        resolved, register_claims=register_claims
-    )
+    cdm_sessions, primary, vocab = _create_cdm_sessions(resolved, register_claims=register_claims)
     try:
         yield cdm_sessions
     finally:
diff --git a/src/omop_graph/extensions/emb.py b/src/omop_graph/extensions/emb.py
index 054ccd2..a8d10e0 100644
--- a/src/omop_graph/extensions/emb.py
+++ b/src/omop_graph/extensions/emb.py
@@ -1,44 +1,39 @@
 from __future__ import annotations
 
-import logging
 import importlib.util
-from typing import TYPE_CHECKING, Optional, Sequence, TypeAlias, Tuple
-import numpy as np
+import logging
+from collections.abc import Sequence
+from typing import TYPE_CHECKING
 
+import numpy as np
 from omop_alchemy.cdm.query import ConceptFilter as CDMConceptFilter
 
 HAS_OMOP_EMB = importlib.util.find_spec("omop_emb") is not None
 
 if TYPE_CHECKING:
     # Optional embedding-specific ones
-    from omop_emb import BackendType, MetricType, IndexType
-    from omop_emb import EmbeddingWriterInterface, EmbeddingReaderInterface
-    from omop_emb import EmbeddingRole
-    from omop_emb.utils.embedding_utils import NearestConceptMatch
-    from omop_emb.utils.embedding_utils import EmbeddingConceptFilter
-
-    EmbeddingBackendType: TypeAlias = BackendType
-    EmbeddingMetricType: TypeAlias = MetricType
-    EmbeddingIndexType: TypeAlias = IndexType
-    EmbeddingProviderType: TypeAlias = str
-    EmbeddingRoleType: TypeAlias = EmbeddingRole
+    from omop_emb import (
+        BackendType,
+        EmbeddingReaderInterface,
+        EmbeddingWriterInterface,
+        MetricType,
+    )
+    from omop_emb.utils.embedding_utils import EmbeddingConceptFilter, NearestConceptMatch
 
     # Circular imports for static type hints
     from omop_graph.graph.kg import KnowledgeGraph
     from omop_graph.graph.paths import StandardConcept
 
-else:
-    EmbeddingBackendType = str
-    EmbeddingMetricType = str
-    EmbeddingIndexType = str
-    EmbeddingProviderType = str
-    EmbeddingRoleType = str
 
-SUPPORTED_BACKENDS: Tuple[str, ...] = ()
-SUPPORTED_METRICS: Tuple[str, ...] = ()
+SUPPORTED_BACKENDS: tuple[str, ...] = ()
+SUPPORTED_METRICS: tuple[str, ...] = ()
 if HAS_OMOP_EMB:
-    from omop_emb import BackendType, EmbeddingRole, MetricType
-    from omop_emb import EmbeddingReaderInterface, EmbeddingWriterInterface
+    from omop_emb import (
+        BackendType,
+        EmbeddingReaderInterface,
+        EmbeddingWriterInterface,
+        MetricType,
+    )
 
     # Extract the string values from the StrEnums
     SUPPORTED_BACKENDS = tuple(v.value for v in BackendType)
@@ -59,7 +54,7 @@ def __init__(self, feature: str = "Embedding functionality"):
 
 def _get_embedding_interface(
     kg: KnowledgeGraph,
-) -> Optional[EmbeddingReaderInterface | EmbeddingWriterInterface]:
+) -> EmbeddingReaderInterface | EmbeddingWriterInterface | None:
     """
     Internal utility to retrieve the embedding interface from the KG, without distinguishing between reader/writer.
     This is only used for internal logic where we don't need to differentiate between reader and writer capabilities.
@@ -74,9 +69,7 @@ def _get_embedding_interface(
     try:
         return kg.emb
     except ValueError:
-        logger.debug(
-            "Embedding interface not available: no EmbeddingConfiguration provided."
-        )
+        logger.debug("Embedding interface not available: no EmbeddingConfiguration provided.")
         return None
     except MissingExtensionError as exc:
         logger.warning(f"Embedding interface not available: {exc}")
@@ -85,7 +78,7 @@ def _get_embedding_interface(
 
 def get_embedding_reader_interface(
     kg: KnowledgeGraph,
-) -> Optional["EmbeddingReaderInterface"]:
+) -> EmbeddingReaderInterface | None:
     """
     Utility to safely retrieve the embedding reader interface from the KG.
     Returns None if the extension is not available or if the interface is not a reader.
@@ -96,15 +89,13 @@ def get_embedding_reader_interface(
         and HAS_OMOP_EMB
         and not isinstance(interface, EmbeddingReaderInterface)
     ):
-        raise TypeError(
-            f"Expected embedding interface to be a reader, but got {type(interface)}."
-        )
+        raise TypeError(f"Expected embedding interface to be a reader, but got {type(interface)}.")
     return interface
 
 
 def get_embedding_writer_interface(
     kg: KnowledgeGraph,
-) -> Optional["EmbeddingWriterInterface"]:
+) -> EmbeddingWriterInterface | None:
     """
     Utility to retrieve the embedding writer interface from the KG when write-capability
     is already guaranteed some other way (e.g. by the caller's own contract). Raises if
@@ -119,22 +110,20 @@ def get_embedding_writer_interface(
     instead.
     """
     interface = _get_embedding_interface(kg)
-    if (
-        interface is not None
-        and HAS_OMOP_EMB
-        and not isinstance(interface, EmbeddingWriterInterface)
-    ):
+    if interface is None:
+        return None
+    if not (HAS_OMOP_EMB and isinstance(interface, EmbeddingWriterInterface)):
         raise TypeError(
             f"Expected embedding interface to be a writer, but got {type(interface)}. "
             "Instantiate the KG with an embedding configuration that has write=True "
             "to get a writer interface."
         )
-    return interface  # ty: ignore[invalid-return-type]
+    return interface
 
 
 def try_get_embedding_writer_interface(
     kg: KnowledgeGraph,
-) -> Optional["EmbeddingWriterInterface"]:
+) -> EmbeddingWriterInterface | None:
     """
     Utility to opportunistically retrieve the embedding writer interface from the KG.
 
@@ -171,7 +160,7 @@ def semantic_similarity(
     kg: KnowledgeGraph,
     standard_concepts: Sequence[StandardConcept],
     query_embedding: np.ndarray,
-) -> Optional[Tuple[Tuple[NearestConceptMatch, ...], ...]]:
+) -> tuple[tuple[NearestConceptMatch, ...], ...] | None:
     """
     Calculates similarity between a query embedding and stored concept embeddings.
 
@@ -197,9 +186,7 @@ def semantic_similarity(
 
     embedding_reader = get_embedding_reader_interface(kg)
     if embedding_reader is None:
-        logger.info(
-            "Embedding reader interface not found in KG. Skipping similarity calculation."
-        )
+        logger.info("Embedding reader interface not found in KG. Skipping similarity calculation.")
         return None
 
     from omop_emb.utils.embedding_utils import EmbeddingConceptFilter
@@ -267,9 +254,9 @@ def semantic_similarity(
 def get_neareast_concepts(
     kg: KnowledgeGraph,
     query_embedding: np.ndarray,
-    concept_filter: Optional[EmbeddingConceptFilter],
-    k: Optional[int] = None,
-) -> Optional[Tuple[Tuple[NearestConceptMatch, ...], ...]]:
+    concept_filter: EmbeddingConceptFilter | None,
+    k: int | None = None,
+) -> tuple[tuple[NearestConceptMatch, ...], ...] | None:
     """
     RAG retrieval for concept similarity scores. The query_embedding is compared against
     stored embeddings using the metric and model already configured on the KG's embedding
@@ -311,8 +298,6 @@ def get_neareast_concepts(
         k=k,
     )
     if not nearest_concepts:
-        logger.info(
-            "No nearest concepts found for the given query embedding and filter."
-        )
+        logger.info("No nearest concepts found for the given query embedding and filter.")
         return None
     return nearest_concepts
diff --git a/src/omop_graph/extensions/omop_alchemy.py b/src/omop_graph/extensions/omop_alchemy.py
index 984a768..7434182 100644
--- a/src/omop_graph/extensions/omop_alchemy.py
+++ b/src/omop_graph/extensions/omop_alchemy.py
@@ -1,19 +1,19 @@
 # Extension to omop-alchemy package
+from dataclasses import dataclass
+from enum import Enum
+from typing import cast
+
 import sqlalchemy as sa
 import sqlalchemy.orm as so
-from orm_loader.helpers import Base, create_tables
+from oa_configurator import ResolvedCDMDatabase, Role
 from omop_alchemy.cdm.base import (
+    CDMTableBase,
     ReferenceTable,
     cdm_table,
-    CDMTableBase,
     merge_table_args,
     role_fk,
 )
-from oa_configurator import ResolvedCDMDatabase, Role
-
-from enum import Enum
-from dataclasses import dataclass
-from typing import cast
+from orm_loader.helpers import Base, create_tables
 
 
 class PredicateKind(Enum):
@@ -58,9 +58,7 @@ class RelationshipClass(ReferenceTable, CDMTableBase, Base):
     predicate_kind: so.Mapped[PredicateKind] = so.mapped_column(
         _predicate_kind_enum(), primary_key=True
     )
-    predicate_subkind: so.Mapped[str] = so.mapped_column(
-        sa.String(20), primary_key=True
-    )
+    predicate_subkind: so.Mapped[str] = so.mapped_column(sa.String(20), primary_key=True)
     description: so.Mapped[str] = so.mapped_column(sa.String(80), nullable=False)
     semantics: so.Mapped[str] = so.mapped_column(sa.String(40), nullable=False)
     inference: so.Mapped[str] = so.mapped_column(sa.String(40), nullable=False)
@@ -82,9 +80,7 @@ class RelationshipMapping(ReferenceTable, CDMTableBase, Base):
     predicate_kind: so.Mapped[PredicateKind] = so.mapped_column(
         _predicate_kind_enum(), primary_key=True
     )
-    predicate_subkind: so.Mapped[str] = so.mapped_column(
-        sa.String(20), primary_key=True
-    )
+    predicate_subkind: so.Mapped[str] = so.mapped_column(sa.String(20), primary_key=True)
 
     # Must match RelationshipClass's schema, or SQLAlchemy silently builds a second, unlinked Table object.
     __table_args__ = merge_table_args(
@@ -150,8 +146,6 @@ def load_relationship_mapping(
     """Load the entire RelationshipMapping table and return it as a dict keyed by relationship_id."""
     results = session.query(RelationshipMapping).all()
     return {
-        row.relationship_id: RelationshipMappingElement.from_relationship_mapping_entry(
-            row
-        )
+        row.relationship_id: RelationshipMappingElement.from_relationship_mapping_entry(row)
         for row in results
     }
diff --git a/src/omop_graph/graph/__init__.py b/src/omop_graph/graph/__init__.py
index ac27018..14522de 100644
--- a/src/omop_graph/graph/__init__.py
+++ b/src/omop_graph/graph/__init__.py
@@ -1,12 +1,12 @@
-from .traverse import traverse
-from .paths import find_shortest_paths, GraphPath, PathStep
 from .kg import KnowledgeGraph, KnowledgeGraphEmbeddingConfiguration
+from .paths import GraphPath, PathStep, find_shortest_paths
+from .traverse import traverse
 
 __all__ = [
-    "traverse",
-    "find_shortest_paths",
     "GraphPath",
-    "PathStep",
     "KnowledgeGraph",
     "KnowledgeGraphEmbeddingConfiguration",
+    "PathStep",
+    "find_shortest_paths",
+    "traverse",
 ]
diff --git a/src/omop_graph/graph/base.py b/src/omop_graph/graph/base.py
index 9e08d4b..b56aa67 100644
--- a/src/omop_graph/graph/base.py
+++ b/src/omop_graph/graph/base.py
@@ -1,7 +1,10 @@
 from __future__ import annotations
+
 from abc import ABC, abstractmethod
+from collections.abc import Iterable
 from datetime import date
-from typing import Iterable, Optional, Literal
+from typing import Literal
+
 from sqlalchemy.orm import Session
 
 from ..extensions.omop_alchemy import PredicateKind
@@ -26,7 +29,7 @@ def predicate_kind(self, relationship_id: str) -> PredicateKind: ...
     def predicate_name(self, relationship_id: str) -> str: ...
 
     @abstractmethod
-    def reverse_predicate_id(self, relationship_id: str) -> Optional[str]: ...
+    def reverse_predicate_id(self, relationship_id: str) -> str | None: ...
 
     @abstractmethod
     def iter_edges(
@@ -35,9 +38,9 @@ def iter_edges(
         concept_ids: int | tuple[int, ...],
         direction: Literal["in", "out"],
         predicate_ids: frozenset[str] | None,
-        predicate_kinds: Optional[frozenset[PredicateKind]] = None,
+        predicate_kinds: frozenset[PredicateKind] | None = None,
         active_only: bool = True,
-        on: Optional[date] = None,
+        on: date | None = None,
         within_domain: bool = False,
     ) -> Iterable[EdgeView]:
         """
diff --git a/src/omop_graph/graph/edges.py b/src/omop_graph/graph/edges.py
index 638348f..0105f12 100644
--- a/src/omop_graph/graph/edges.py
+++ b/src/omop_graph/graph/edges.py
@@ -20,8 +20,7 @@
 import logging
 from dataclasses import dataclass
 from datetime import date
-from typing import TYPE_CHECKING, Optional
-
+from typing import TYPE_CHECKING
 
 from omop_alchemy.cdm.model import normalised_flag
 
@@ -61,9 +60,9 @@ class EdgeView:
     subject_id: int
     predicate_id: str
     object_id: int
-    valid_start_date: Optional[date]
-    valid_end_date: Optional[date]
-    invalid_reason: Optional[str]
+    valid_start_date: date | None
+    valid_end_date: date | None
+    invalid_reason: str | None
     predicate_kind: PredicateKind
     predicate_subkind: str
 
@@ -118,7 +117,7 @@ class Predicate:
 
     relationship_id: str
     name: str
-    reverse_id: Optional[str]
+    reverse_id: str | None
     is_hierarchical: bool
     anc_up: bool
     anc_down: bool
@@ -147,11 +146,11 @@ def __repr__(self) -> str:
 
 
 def is_active(
-    start: Optional[date],
-    end: Optional[date],
-    invalid_reason: Optional[str],
+    start: date | None,
+    end: date | None,
+    invalid_reason: str | None,
     *,
-    on: Optional[date] = None,
+    on: date | None = None,
 ) -> bool:
     """
     Check if a relationship is active on a given date.
diff --git a/src/omop_graph/graph/kg.py b/src/omop_graph/graph/kg.py
index 0072510..f507dbd 100644
--- a/src/omop_graph/graph/kg.py
+++ b/src/omop_graph/graph/kg.py
@@ -17,32 +17,31 @@
 import functools
 import logging
 import re
-from datetime import date
 from collections import defaultdict
-from typing import Callable, Dict, Optional, Tuple, Literal, Generator, TYPE_CHECKING
+from collections.abc import Callable, Generator
 from dataclasses import dataclass
+from datetime import date
+from typing import TYPE_CHECKING, Literal
 
-from sqlalchemy import Row
-from sqlalchemy.orm import Session
-from sqlalchemy.engine import Engine
+from oa_configurator import ResolvedModel
 from omop_alchemy.backends import BackendNotSupportedError, FullTextError
 from omop_alchemy.cdm.model.vocabulary import Concept
 from omop_alchemy.cdm.query import ConceptFilter
-from oa_configurator import ResolvedModel
+from sqlalchemy import Row
+from sqlalchemy.engine import Engine
+from sqlalchemy.orm import Session
 
 if TYPE_CHECKING:
     from omop_emb import (
         EmbeddingBackend,
+        EmbeddingReaderInterface,
         EmbeddingStoreReader,
         EmbeddingWriterInterface,
-        EmbeddingReaderInterface,
+        MetricType,
     )
 
 # Local Application Imports
-from ..extensions.emb import (
-    MissingExtensionError,
-    EmbeddingMetricType,
-)
+from ..extensions.emb import MissingExtensionError
 from ..extensions.omop_alchemy import (
     PredicateKind,
     RelationshipMappingElement,
@@ -58,6 +57,7 @@
 )
 from .queries import (
     q_all_predicates_with_ancestry,
+    q_children,
     q_concept_domain_ids,
     q_concept_id_by_code,
     q_concept_name_fulltext,
@@ -71,15 +71,14 @@
     q_concept_views,
     q_concept_vocabulary_ids,
     q_edges,
+    q_entities,
     q_leaves,
     q_parents,
-    q_children,
     q_predicate_name,
     q_predicate_row_with_ancestry,
+    q_relationships,
     q_roots,
     q_singletons,
-    q_entities,
-    q_relationships,
 )
 
 logger = logging.getLogger(__name__)
@@ -92,13 +91,13 @@ class KnowledgeGraphEmbeddingConfiguration:
 
     A complete configuration: whenever embedding support is used at all
     (read or write), a real backend and a real resolved model are both
-    required. The caller resolves the vector store and the model, 
+    required. The caller resolves the vector store and the model,
     builds the backend, and passes both in here. Used to enhance the
     knowledge graph with embedding-based grounding and similarity scoring.
 
     Parameters
     ----------
-    metric_type : EmbeddingMetricType
+    metric_type : MetricType
         The similarity/distance metric to use for embedding comparisons (e.g., cosine, euclidean).
         This is required to ensure that the correct type of index is used in the backend and that
         similarity computations are consistent.
@@ -126,12 +125,12 @@ class KnowledgeGraphEmbeddingConfiguration:
         straight through to ``EmbeddingReaderInterface``.
     """
 
-    metric_type: EmbeddingMetricType
-    backend: "EmbeddingBackend | EmbeddingStoreReader"
+    metric_type: MetricType
+    backend: EmbeddingBackend | EmbeddingStoreReader
     resolved_model: ResolvedModel
     write: bool = False
     compute_missing_embeddings: bool = False
-    faiss_cache_dir: Optional[str] = None
+    faiss_cache_dir: str | None = None
 
     def __post_init__(self) -> None:
         if self.compute_missing_embeddings and not self.write:
@@ -187,7 +186,7 @@ def __init__(
         self,
         cdm_sessions: Callable[[], Session],
         *,
-        emb_config: Optional[KnowledgeGraphEmbeddingConfiguration] = None,
+        emb_config: KnowledgeGraphEmbeddingConfiguration | None = None,
     ):
         if isinstance(cdm_sessions, Engine) or not callable(cdm_sessions):
             raise TypeError(
@@ -218,7 +217,7 @@ def __init__(
         self._emb = None
 
     @property
-    def emb(self) -> "EmbeddingWriterInterface | EmbeddingReaderInterface":
+    def emb(self) -> EmbeddingWriterInterface | EmbeddingReaderInterface:
         """Namespace for all embedding operations.
 
         Returns an ``EmbeddingWriterInterface`` when ``self._emb_config.write`` is True,
@@ -234,8 +233,8 @@ def emb(self) -> "EmbeddingWriterInterface | EmbeddingReaderInterface":
 
         try:
             from omop_emb.interface import (
-                EmbeddingWriterInterface,
                 EmbeddingReaderInterface,
+                EmbeddingWriterInterface,
             )
 
             if self._emb_config is None:
@@ -283,23 +282,21 @@ def emb(self) -> "EmbeddingWriterInterface | EmbeddingReaderInterface":
                 )
                 raise MissingExtensionError() from e
             raise
-        except ImportError as e:
+        except ImportError:
             logger.info(
                 "Embedding functionality failed to initialize due to an import error in the optional embedding stack."
             )
-            raise e
+            raise
 
     @property
-    def embedding_configuration(self) -> Optional[KnowledgeGraphEmbeddingConfiguration]:
+    def embedding_configuration(self) -> KnowledgeGraphEmbeddingConfiguration | None:
         """Returns the current embedding configuration, if set."""
         return self._emb_config
 
     @property
     def compute_missing_embeddings(self) -> bool:
         """Indicates whether on-the-fly computation of missing concept embeddings is enabled."""
-        return (
-            self._emb_config.compute_missing_embeddings if self._emb_config else False
-        )
+        return self._emb_config.compute_missing_embeddings if self._emb_config else False
 
     def concept_view(self, concept_id: int) -> ConceptView:
         """
@@ -360,9 +357,7 @@ def concept_id_by_code(self, vocabulary_id: str, concept_code: str) -> int:
         """
         with self.session_factory() as session:
             concept_id = int(
-                session.execute(
-                    q_concept_id_by_code(vocabulary_id, concept_code)
-                ).scalar_one()
+                session.execute(q_concept_id_by_code(vocabulary_id, concept_code)).scalar_one()
             )
         return concept_id
 
@@ -371,7 +366,7 @@ def concept_lookup(
         query_term: str,
         match_kind: LabelMatchKind,
         synonym: bool = False,
-        search_constraint: Optional[ConceptFilter] = None,
+        search_constraint: ConceptFilter | None = None,
         sort: bool = True,
     ) -> tuple[LabelMatch, ...]:
         """
@@ -429,7 +424,7 @@ def concept_lookup(
             )
         return matches
 
-    def concept_ids_by_label(self, label: str) -> Tuple[int, ...]:
+    def concept_ids_by_label(self, label: str) -> tuple[int, ...]:
         """
         Find concept IDs that match the label exactly (case-insensitive).
         """
@@ -462,9 +457,7 @@ def predicate_name(self, relationship_id: str) -> str:
         """
         # TODO: Not really necessary. The "ID" is mostly human-readable anyways.
         with self.session_factory() as session:
-            predicate_name = session.execute(
-                q_predicate_name(relationship_id)
-            ).scalar_one()
+            predicate_name = session.execute(q_predicate_name(relationship_id)).scalar_one()
         return predicate_name
 
     def predicate_kind(self, relationship_id: str) -> PredicateKind:
@@ -480,9 +473,7 @@ def _mapping_for(self, relationship_id: str) -> RelationshipMappingElement:
             raise AttributeError(f"`{relationship_id}` not in relationship mapping.")
         return item
 
-    def predicate_kinds(
-        self, relationship_ids: tuple[str, ...]
-    ) -> Tuple[PredicateKind, ...]:
+    def predicate_kinds(self, relationship_ids: tuple[str, ...]) -> tuple[PredicateKind, ...]:
         """
         Classify a batch of predicates.
         """
@@ -494,7 +485,7 @@ def relationships(
         predicates: tuple[str, ...] | None,
         objects: tuple[int, ...] | None,
         invert: bool = False,
-    ) -> Generator[Tuple[int, str, int], None, None]:
+    ) -> Generator[tuple[int, str, int], None, None]:
         """
         Query relationships between concepts.
 
@@ -534,7 +525,7 @@ def relationships(
             ):
                 yield s, p, o
 
-    def reverse_predicate_id(self, relationship_id: str) -> Optional[str]:
+    def reverse_predicate_id(self, relationship_id: str) -> str | None:
         """
         Get the reverse relationship ID, if it exists.
         """
@@ -548,12 +539,12 @@ def _normalise_query_term(self, query_term: str) -> str:
 
     def edges(
         self,
-        concept_ids: Tuple[int, ...] | int,
+        concept_ids: tuple[int, ...] | int,
         direction: Literal["in", "out"],
-        predicate_ids: Optional[frozenset[str]] = None,
-        predicate_kinds: Optional[frozenset[PredicateKind]] = None,
+        predicate_ids: frozenset[str] | None = None,
+        predicate_kinds: frozenset[PredicateKind] | None = None,
         active_only: bool = True,
-        on: Optional[date] = None,
+        on: date | None = None,
         within_domain: bool = False,
     ) -> tuple[EdgeView, ...]:
         """
@@ -600,10 +591,10 @@ def iter_edges(
         session: Session,
         concept_ids: int | tuple[int, ...],
         direction: Literal["in", "out"],
-        predicate_ids: Optional[frozenset[str]] = None,
-        predicate_kinds: Optional[frozenset[PredicateKind]] = None,
+        predicate_ids: frozenset[str] | None = None,
+        predicate_kinds: frozenset[PredicateKind] | None = None,
         active_only: bool = True,
-        on: Optional[date] = None,
+        on: date | None = None,
         within_domain: bool = False,
     ) -> Generator[EdgeView, None, None]:
         """Yield edges of *concept_ids* on *session*, classified from the relationship mapping.
@@ -691,9 +682,7 @@ def roots(
         """
         with self.session_factory() as session:
             roots = tuple(
-                session.execute(
-                    q_roots(domain_id=domain_id, vocabulary_id=vocabulary_id)
-                ).scalars()
+                session.execute(q_roots(domain_id=domain_id, vocabulary_id=vocabulary_id)).scalars()
             )
         return roots
 
@@ -756,17 +745,13 @@ def _valid_vocabularies(self) -> frozenset[str]:
             rows = session.execute(q_concept_vocabulary_ids()).all()
         return frozenset(row.vocabulary_id for row in rows)
 
-    def get_potential_ancestor(
-        self, child_id: int, parent_id: int
-    ) -> Optional[AncestorMatch]:
+    def get_potential_ancestor(self, child_id: int, parent_id: int) -> AncestorMatch | None:
         """
         Check if an ancestry relationship exists between a child and parent.
         """
 
         with self.session_factory() as session:
-            row = session.execute(
-                q_concept_potential_ancestor(child_id, parent_id)
-            ).first()
+            row = session.execute(q_concept_potential_ancestor(child_id, parent_id)).first()
 
         if row is None:
             return None
@@ -778,9 +763,9 @@ def get_potential_ancestor(
 
     def get_potential_ancestors_batch(
         self,
-        child_ids: Tuple[int, ...],
-        parent_ids: Tuple[int, ...],
-    ) -> Dict[int, Dict[int, AncestorMatch]]:
+        child_ids: tuple[int, ...],
+        parent_ids: tuple[int, ...],
+    ) -> dict[int, dict[int, AncestorMatch]]:
         """Check which candidate parents are ancestors of one or more children.
 
         Parameters
@@ -799,11 +784,9 @@ def get_potential_ancestors_batch(
             return {}
 
         with self.session_factory() as session:
-            rows = session.execute(
-                q_concept_potential_ancestors_batch(child_ids, parent_ids)
-            ).all()
+            rows = session.execute(q_concept_potential_ancestors_batch(child_ids, parent_ids)).all()
 
-        result: Dict[int, Dict[int, AncestorMatch]] = defaultdict(dict)
+        result: dict[int, dict[int, AncestorMatch]] = defaultdict(dict)
         for row in rows:
             result[row.descendant_concept_id][row.ancestor_concept_id] = AncestorMatch(
                 ancestor_concept_id=row.ancestor_concept_id,
@@ -812,7 +795,7 @@ def get_potential_ancestors_batch(
             )
         return result
 
-    def get_num_ancestors(self, concept_ids: tuple[int, ...]) -> Dict[int, int]:
+    def get_num_ancestors(self, concept_ids: tuple[int, ...]) -> dict[int, int]:
         """
         Get the count of ancestors for a batch of concepts.
         """
@@ -830,9 +813,7 @@ def check_search_constraints(self, constraints: ConceptFilter) -> None:
                 )
 
         if constraints.vocabularies is not None:
-            invalid = [
-                v for v in constraints.vocabularies if v not in self._valid_vocabularies
-            ]
+            invalid = [v for v in constraints.vocabularies if v not in self._valid_vocabularies]
             if invalid:
                 raise ValueError(
                     f"Invalid vocabulary constraint(s): {invalid}. "
diff --git a/src/omop_graph/graph/nodes.py b/src/omop_graph/graph/nodes.py
index 49ac585..846e08e 100644
--- a/src/omop_graph/graph/nodes.py
+++ b/src/omop_graph/graph/nodes.py
@@ -14,16 +14,15 @@
 
 import logging
 from collections import defaultdict
+from collections.abc import Iterable
 from dataclasses import dataclass
 from datetime import date
 from enum import Enum
 from html import escape
 from itertools import chain
-from typing import Dict, Iterable, List, Optional, Tuple
-
-from sqlalchemy import Row
 
 from omop_alchemy.cdm.model import normalised_flag
+from sqlalchemy import Row
 
 logger = logging.getLogger(__name__)
 
@@ -75,7 +74,7 @@ class ConceptView:
     standard_concept: bool
     valid_start_date: date
     valid_end_date: date
-    invalid_reason: Optional[str]
+    invalid_reason: str | None
     # Defaulted so rows projected before the classification label was added
     # still construct; the query layer always supplies it.
     classification_concept: bool = False
@@ -186,7 +185,7 @@ class LabelMatchKind(Enum):
     PARTIAL = 2
     EMBEDDING = 3
 
-    def __lt__(self, other: "LabelMatchKind") -> bool:
+    def __lt__(self, other: LabelMatchKind) -> bool:
         return self.value < other.value
 
 
@@ -275,7 +274,7 @@ class LabelMatchGroupView:
         A dictionary mapping Concept ID to a tuple of sorted LabelMatches.
     """
 
-    groups: Dict[int, Tuple[LabelMatch, ...]]
+    groups: dict[int, tuple[LabelMatch, ...]]
 
     @classmethod
     def from_matches(cls, matches: Iterable[LabelMatch]) -> LabelMatchGroupView:
@@ -298,13 +297,12 @@ def from_matches(cls, matches: Iterable[LabelMatch]) -> LabelMatchGroupView:
         LabelMatchGroupView
             The grouped view.
         """
-        grouped: Dict[int, List[LabelMatch]] = defaultdict(list)
+        grouped: dict[int, list[LabelMatch]] = defaultdict(list)
         for m in matches:
             grouped[m.matched_concept_id].append(m)
 
         grouped_tuple = {
-            cid: tuple(sorted(ms, key=lambda m: m.match_kind.value))
-            for cid, ms in grouped.items()
+            cid: tuple(sorted(ms, key=lambda m: m.match_kind.value)) for cid, ms in grouped.items()
         }
         return cls(groups=grouped_tuple)
 
diff --git a/src/omop_graph/graph/paths.py b/src/omop_graph/graph/paths.py
index 1fda7d2..5981600 100644
--- a/src/omop_graph/graph/paths.py
+++ b/src/omop_graph/graph/paths.py
@@ -20,19 +20,13 @@
 from typing import (
     TYPE_CHECKING,
     Any,
-    Dict,
-    List,
-    Optional,
-    Set,
-    Tuple,
-    Union,
 )
 
 # Local Application Imports
 from omop_graph.extensions.omop_alchemy import PredicateKind
 from omop_graph.graph.edges import EdgeView
-from omop_graph.graph.traverse import GraphTrace, TraceStep
 from omop_graph.graph.nodes import LabelMatchKind
+from omop_graph.graph.traverse import GraphTrace, TraceStep
 from omop_graph.reasoning.resolvers import CandidateHit
 
 if TYPE_CHECKING:
@@ -105,7 +99,7 @@ def start_concept_id(self) -> int:
             raise ValueError("Empty path has no start concept")
         return self.steps[0].subject.concept_id
 
-    def get_first_standard_concept_id(self) -> Optional[int]:
+    def get_first_standard_concept_id(self) -> int | None:
         """
         Find the ID of the first Standard Concept encountered in the path.
 
@@ -127,9 +121,7 @@ def nodes(self) -> tuple[int, ...]:
         """
         if not self.steps:
             return ()
-        return (self.steps[0].subject.concept_id,) + tuple(
-            s.object.concept_id for s in self.steps
-        )
+        return (self.steps[0].subject.concept_id,) + tuple(s.object.concept_id for s in self.steps)
 
     def __getitem__(self, index):
         return self.steps[index]
@@ -142,7 +134,7 @@ def __repr__(self) -> str:
             return "GraphPath(<empty>)"
         return f"GraphPath(len={len(self.steps)})"
 
-    def explain(self, kg: "KnowledgeGraph") -> str:
+    def explain(self, kg: KnowledgeGraph) -> str:
         """
         Generate a human-readable string explaining the path.
 
@@ -176,7 +168,7 @@ def reconstruct_paths(
     meet,
     parents_fwd,
     parents_bwd,
-    concept_standard_map: Dict[int, bool],
+    concept_standard_map: dict[int, bool],
 ):
     """
     Reconstruct full paths from bidirectional BFS parent pointers.
@@ -214,16 +206,16 @@ def right(n):
 
 
 def find_shortest_paths(
-    kg: "KnowledgeGraph",
+    kg: KnowledgeGraph,
     source: int,
     target: int,
-    predicate_kinds: Optional[frozenset[PredicateKind]] = None,
+    predicate_kinds: frozenset[PredicateKind] | None = None,
     max_depth: int = 6,
-    on: Optional[Any] = None,
+    on: Any | None = None,
     max_paths: int = 20,
     traced: bool = False,
     within_domain: bool = True,
-) -> Tuple[List[GraphPath], Optional[GraphTrace]]:
+) -> tuple[list[GraphPath], GraphTrace | None]:
     """
     Find shortest paths between source and target using bidirectional BFS.
 
@@ -258,9 +250,7 @@ def find_shortest_paths(
     if source == target:
         path = GraphPath(steps=())
         trace = (
-            GraphTrace(
-                seeds=(source,), steps=[], terminated_reason="source_equals_target"
-            )
+            GraphTrace(seeds=(source,), steps=[], terminated_reason="source_equals_target")
             if traced
             else None
         )
@@ -272,16 +262,16 @@ def find_shortest_paths(
     depth_fwd = {source: 0}
     depth_bwd = {target: 0}
 
-    parents_fwd: Dict[int, List[Tuple[int, str]]] = defaultdict(list)
-    parents_bwd: Dict[int, List[Tuple[int, str]]] = defaultdict(list)
+    parents_fwd: dict[int, list[tuple[int, str]]] = defaultdict(list)
+    parents_bwd: dict[int, list[tuple[int, str]]] = defaultdict(list)
 
-    best_total_depth: Optional[int] = None
-    meeting_nodes: Set[int] = set()
-    trace_steps: List[TraceStep] = []
+    best_total_depth: int | None = None
+    meeting_nodes: set[int] = set()
+    trace_steps: list[TraceStep] = []
 
     while q_fwd and q_bwd:
         expand_forward = len(q_fwd) <= len(q_bwd)
-        expanded: List[EdgeView] = []
+        expanded: list[EdgeView] = []
 
         if expand_forward:
             cur = q_fwd.popleft()
@@ -359,9 +349,7 @@ def find_shortest_paths(
                             meeting_nodes.add(prev)
 
         if traced:
-            trace_steps.append(
-                TraceStep(depth=d, node=cur, expanded_edges=tuple(expanded))
-            )
+            trace_steps.append(TraceStep(depth=d, node=cur, expanded_edges=tuple(expanded)))
 
         # Stop if we found a path and current searches exceed optimal depth
         if best_total_depth is not None:
@@ -390,16 +378,14 @@ def find_shortest_paths(
     # One batched lookup to get is_standard for every discovered concept so that
     # reconstructed Node objects carry the correct flag (zero extra per-node DB calls).
     all_discovered = tuple(set(depth_fwd.keys()) | set(depth_bwd.keys()))
-    concept_standard_map: Dict[int, bool] = {
+    concept_standard_map: dict[int, bool] = {
         v.concept_id: v.standard_concept for v in kg.concept_views(all_discovered)
     }
 
-    paths: List[GraphPath] = []
+    paths: list[GraphPath] = []
     for meet in meeting_nodes:
         paths.extend(
-            reconstruct_paths(
-                source, target, meet, parents_fwd, parents_bwd, concept_standard_map
-            )
+            reconstruct_paths(source, target, meet, parents_fwd, parents_bwd, concept_standard_map)
         )
         if len(paths) >= max_paths:
             break
@@ -418,15 +404,15 @@ def find_shortest_paths(
 
 
 def find_shortest_paths_batch(
-    kg: "KnowledgeGraph",
+    kg: KnowledgeGraph,
     source: int,
     target: int,
-    predicate_kinds: Union[Set[PredicateKind], frozenset[PredicateKind], None] = None,
+    predicate_kinds: set[PredicateKind] | frozenset[PredicateKind] | None = None,
     max_depth: int = 6,
-    on: Optional[Any] = None,
+    on: Any | None = None,
     max_paths: int = 20,
     within_domain: bool = True,
-) -> List[GraphPath]:
+) -> list[GraphPath]:
     """
     Find shortest paths using an optimized batch-BFS approach.
 
@@ -471,11 +457,11 @@ def find_shortest_paths_batch(
     depth_bwd = {target: 0}
 
     # Parents for path reconstruction
-    parents_fwd: Dict[int, List[Tuple[int, str]]] = defaultdict(list)
-    parents_bwd: Dict[int, List[Tuple[int, str]]] = defaultdict(list)
+    parents_fwd: dict[int, list[tuple[int, str]]] = defaultdict(list)
+    parents_bwd: dict[int, list[tuple[int, str]]] = defaultdict(list)
 
-    best_total_depth: Optional[int] = None
-    meeting_nodes: Set[int] = set()
+    best_total_depth: int | None = None
+    meeting_nodes: set[int] = set()
 
     # Loop until frontiers are empty
     while fwd_frontier and bwd_frontier:
@@ -544,9 +530,7 @@ def find_shortest_paths_batch(
         # 4. Stop Condition check
         if best_total_depth is not None:
             # Shallowest possible node in the NEXT layer we just built
-            min_current = min(
-                (current_depth_map[n] for n in next_frontier), default=999
-            )
+            min_current = min((current_depth_map[n] for n in next_frontier), default=999)
 
             # Shallowest possible node waiting in the OTHER frontier
             min_other = min((other_depth_map[n] for n in other_frontier), default=999)
@@ -565,16 +549,14 @@ def find_shortest_paths_batch(
         return []
 
     all_discovered = tuple(set(depth_fwd.keys()) | set(depth_bwd.keys()))
-    concept_standard_map: Dict[int, bool] = {
+    concept_standard_map: dict[int, bool] = {
         v.concept_id: v.standard_concept for v in kg.concept_views(all_discovered)
     }
 
-    paths: List[GraphPath] = []
+    paths: list[GraphPath] = []
     for meet in meeting_nodes:
         paths.extend(
-            reconstruct_paths(
-                source, target, meet, parents_fwd, parents_bwd, concept_standard_map
-            )
+            reconstruct_paths(source, target, meet, parents_fwd, parents_bwd, concept_standard_map)
         )
         if len(paths) >= max_paths:
             break
@@ -620,7 +602,7 @@ class StandardConcept:
         How far this standard concept is from where it needs to be, with a meaning that
         depends on whether ancestor targets were given to ``find_standard_paths``:
             - Targets given (ancestor-constrained grounding): this is the ancestor-
-            hierarchy distance (via ``concept_ancestor.min_levels_of_separation``) 
+            hierarchy distance (via ``concept_ancestor.min_levels_of_separation``)
             from this concept to the required parent.
             - No targets given (unconstrained grounding): this is the hop count
             from the original found concept to the standard concept.
@@ -657,8 +639,8 @@ class StandardConcept:
 
 
 def get_unique_standard_concepts(
-    concepts: List[StandardConcept],
-) -> List[StandardConcept]:
+    concepts: list[StandardConcept],
+) -> list[StandardConcept]:
     """
     Filter a list of StandardConcepts to keep only the best match per Concept ID.
 
@@ -685,14 +667,14 @@ def get_unique_standard_concepts(
 
 
 def find_standard_paths(
-    kg: "KnowledgeGraph",
-    targets: Optional[Tuple[int, ...]],
+    kg: KnowledgeGraph,
+    targets: tuple[int, ...] | None,
     candidate: CandidateHit,
-    predicate_kinds: Optional[frozenset[Any]] = None,
+    predicate_kinds: frozenset[Any] | None = None,
     max_depth: int = 6,
-    max_concepts: Optional[int] = None,
+    max_concepts: int | None = None,
     within_domain: bool = True,
-) -> List[StandardConcept]:
+) -> list[StandardConcept]:
     """
     Search for standard concepts reachable from a candidate, optionally verified against
     ancestor targets.
@@ -714,9 +696,9 @@ def find_standard_paths(
 
     2. When ``targets`` is None or empty, there is no ancestor to verify against: the
     first standard concept reached on each branch is accepted directly and not expanded
-    further. This is a looser, unconstrained form of grounding. It "grounds" a 
-    candidate to its standard form without being able to disambiguate against a known 
-    hierarchy branch. See ``StandardConcept.separation`` for how distance is measured in 
+    further. This is a looser, unconstrained form of grounding. It "grounds" a
+    candidate to its standard form without being able to disambiguate against a known
+    hierarchy branch. See ``StandardConcept.separation`` for how distance is measured in
     this mode.
 
     Parameters
@@ -781,16 +763,14 @@ def find_standard_paths(
     ]
     # Track the shallowest iteration we have enqueued per concept to avoid
     # unbounded duplicate growth in high-degree neighborhoods.
-    visited_min_iteration: Dict[int, int] = {candidate.concept_id: 0}
+    visited_min_iteration: dict[int, int] = {candidate.concept_id: 0}
 
-    found_standard_concepts: Dict[int, List[StandardConcept]] = (
-        {target_id: [] for target_id in targets}
-        if targets
-        else {unconstrained_placeholder: []}
+    found_standard_concepts: dict[int, list[StandardConcept]] = (
+        {target_id: [] for target_id in targets} if targets else {unconstrained_placeholder: []}
     )
 
     while queue:
-        wave: List[QueueItem] = []
+        wave: list[QueueItem] = []
         while queue:
             # Drain the entire heap to process them at once and reduce round-trips to DB for ancestor checks.
             wave.append(heapq.heappop(queue))
@@ -805,7 +785,7 @@ def find_standard_paths(
 
         if standard_items:
             # Dedup
-            dedup_standard_items: Dict[int, QueueItem] = {}
+            dedup_standard_items: dict[int, QueueItem] = {}
             for item in standard_items:
                 if item.node.concept_id not in dedup_standard_items:
                     dedup_standard_items[item.node.concept_id] = item
@@ -900,17 +880,14 @@ def find_standard_paths(
                     continue
 
                 next_iterations = iterations + 1
-                
+
                 # Early-stop for unconstrained mode:
                 # Number of hops from candidate to standard concept
-                # exceeds max_depth to prevent infinite expansion 
+                # exceeds max_depth to prevent infinite expansion
                 if not targets and next_iterations > max_depth:
                     continue
                 prev_best_iteration = visited_min_iteration.get(object_id)
-                if (
-                    prev_best_iteration is not None
-                    and prev_best_iteration <= next_iterations
-                ):
+                if prev_best_iteration is not None and prev_best_iteration <= next_iterations:
                     continue
                 visited_min_iteration[object_id] = next_iterations
 
@@ -969,11 +946,11 @@ def __repr__(self) -> str:
     @classmethod
     def from_path(
         cls,
-        kg: "KnowledgeGraph",
+        kg: KnowledgeGraph,
         path: GraphPath,
         match_kind: LabelMatchKind,
-        source_concept_id: Optional[int] = None,
-    ) -> "PathProfile":
+        source_concept_id: int | None = None,
+    ) -> PathProfile:
         """
         Analyze a path to determine the 'Standard Anchor'.
 
@@ -1013,7 +990,7 @@ def get_view(idx):
             return view_map[node_ids[idx]]
 
         predicate_kinds = kg.predicate_kinds(tuple(p.predicate for p in path.steps))
-        standard_anchor: Optional[Tuple[int, str]] = None
+        standard_anchor: tuple[int, str] | None = None
 
         for step_idx in range(len(path.steps)):
             next_view = get_view(step_idx + 1)
@@ -1051,7 +1028,7 @@ class PathExplanationStep:
     """
 
     step: PathStep
-    traversal_depth: Optional[int]
+    traversal_depth: int | None
     predicate_kind: PredicateKind
     reason: str
 
@@ -1069,23 +1046,21 @@ class PathExplanation:
     @classmethod
     def from_path(
         cls,
-        kg: "KnowledgeGraph",
+        kg: KnowledgeGraph,
         path: GraphPath,
         trace: GraphTrace,
         match_kind: LabelMatchKind,
-    ) -> "PathExplanation":
+    ) -> PathExplanation:
         """
         Construct an explanation by combining the path, the trace log, and semantic profiles.
         """
-        steps: List[PathExplanationStep] = []
+        steps: list[PathExplanationStep] = []
         source = (
             path.steps[0].subject.concept_id
             if path.steps
             else (trace.seeds[0] if trace.seeds else None)
         )
-        profile = PathProfile.from_path(
-            kg, path, match_kind=match_kind, source_concept_id=source
-        )
+        profile = PathProfile.from_path(kg, path, match_kind=match_kind, source_concept_id=source)
 
         for step in path.steps:
             ts = trace_contains_step(trace, step)
@@ -1106,7 +1081,7 @@ def from_path(
         )
 
 
-def trace_contains_step(trace: GraphTrace, step: PathStep) -> Optional[TraceStep]:
+def trace_contains_step(trace: GraphTrace, step: PathStep) -> TraceStep | None:
     """
     Check if a specific path step appears in the search trace.
     """
@@ -1114,9 +1089,6 @@ def trace_contains_step(trace: GraphTrace, step: PathStep) -> Optional[TraceStep
         if ts.node != step.subject.concept_id:
             continue
         for e in ts.expanded_edges:
-            if (
-                e.object_id == step.object.concept_id
-                and e.predicate_id == step.predicate
-            ):
+            if e.object_id == step.object.concept_id and e.predicate_id == step.predicate:
                 return ts
     return None
diff --git a/src/omop_graph/graph/queries.py b/src/omop_graph/graph/queries.py
index f561979..b1dafc1 100644
--- a/src/omop_graph/graph/queries.py
+++ b/src/omop_graph/graph/queries.py
@@ -14,24 +14,8 @@
 
 from __future__ import annotations
 
-from typing import Optional, Tuple, Literal, Union, cast
 from datetime import date
-
-from sqlalchemy import (
-    and_,
-    case,
-    exists,
-    func,
-    literal,
-    or_,
-    select,
-    Connection,
-    Engine,
-    Table,
-)
-from sqlalchemy.orm import aliased
-from sqlalchemy.sql import Select
-
+from typing import Literal, cast
 
 from omop_alchemy.backends import (
     resolve_backend,
@@ -44,6 +28,20 @@
     Relationship,
 )
 from omop_alchemy.cdm.query import ConceptFilter
+from sqlalchemy import (
+    Connection,
+    Engine,
+    Table,
+    and_,
+    case,
+    exists,
+    func,
+    literal,
+    or_,
+    select,
+)
+from sqlalchemy.orm import aliased
+from sqlalchemy.sql import Select
 
 from ..extensions.omop_alchemy import RelationshipMapping
 
@@ -135,7 +133,7 @@ def q_concept_view(concept_id: int) -> Select:
     ).where(Concept.concept_id == concept_id)
 
 
-def q_concept_views(concept_ids: Tuple[int, ...], sort: bool = True) -> Select:
+def q_concept_views(concept_ids: tuple[int, ...], sort: bool = True) -> Select:
     """
     Query for multiple concepts by their IDs.
 
@@ -228,7 +226,7 @@ def q_concept_synonym() -> Select:
 
 def q_concept_name_match(
     query_concept_name: str,
-    search_constraint: Optional[ConceptFilter] = None,
+    search_constraint: ConceptFilter | None = None,
     synonym: bool = False,
     sort: bool = True,
 ) -> Select:
@@ -249,23 +247,17 @@ def q_concept_name_match(
     Select
         The query statement.
     """
-    name_expr = (
-        Concept_Synonym.concept_synonym_name if synonym else Concept.concept_name
-    )
+    name_expr = Concept_Synonym.concept_synonym_name if synonym else Concept.concept_name
 
     if synonym:
         base_stmt = q_concept_synonym().where(
             func.lower(name_expr) == func.lower(query_concept_name)
         )
     else:
-        base_stmt = q_concept_name().where(
-            func.lower(name_expr) == func.lower(query_concept_name)
-        )
+        base_stmt = q_concept_name().where(func.lower(name_expr) == func.lower(query_concept_name))
     if search_constraint:
         if not isinstance(search_constraint, ConceptFilter):
-            raise TypeError(
-                "search_constraint must be an instance of ConceptFilter"
-            )
+            raise TypeError("search_constraint must be an instance of ConceptFilter")
         base_stmt = search_constraint.apply(base_stmt)
     if sort:
         base_stmt = base_stmt.order_by(*_concept_match_order_terms(name_expr))
@@ -274,7 +266,7 @@ def q_concept_name_match(
 
 def q_concept_name_ilike(
     query_concept_name: str,
-    search_constraint: Optional[ConceptFilter] = None,
+    search_constraint: ConceptFilter | None = None,
     synonym: bool = False,
     sort: bool = True,
 ) -> Select:
@@ -295,24 +287,18 @@ def q_concept_name_ilike(
     Select
         The query statement.
     """
-    name_expr = (
-        Concept_Synonym.concept_synonym_name if synonym else Concept.concept_name
-    )
+    name_expr = Concept_Synonym.concept_synonym_name if synonym else Concept.concept_name
 
     if "%" in query_concept_name:
         raise ValueError("query_concept_name should not contain wildcards like '%'.")
 
     if synonym:
-        base_stmt = q_concept_synonym().where(
-            name_expr.ilike(f"%{query_concept_name}%")
-        )
+        base_stmt = q_concept_synonym().where(name_expr.ilike(f"%{query_concept_name}%"))
     else:
         base_stmt = q_concept_name().where(name_expr.ilike(f"%{query_concept_name}%"))
     if search_constraint:
         if not isinstance(search_constraint, ConceptFilter):
-            raise TypeError(
-                "search_constraint must be an instance of ConceptFilter"
-            )
+            raise TypeError("search_constraint must be an instance of ConceptFilter")
         base_stmt = search_constraint.apply(base_stmt)
     if sort:
         base_stmt = base_stmt.order_by(*_concept_match_order_terms(name_expr))
@@ -323,7 +309,7 @@ def q_concept_name_fulltext(
     query_concept_name: str,
     *,
     engine: Engine | Connection,
-    search_constraint: Optional[ConceptFilter] = None,
+    search_constraint: ConceptFilter | None = None,
     synonym: bool = False,
     sort: bool = True,
 ) -> Select:
@@ -354,12 +340,12 @@ def q_concept_name_fulltext(
     FullTextError
         If the stored tsvector column is missing.
     """
-    name_expr = (
-        Concept_Synonym.concept_synonym_name if synonym else Concept.concept_name
-    )
+    name_expr = Concept_Synonym.concept_synonym_name if synonym else Concept.concept_name
     target_table = Concept_Synonym if synonym else Concept
     stmt = q_concept_synonym() if synonym else q_concept_name()
-    vector = resolve_backend(engine).fulltext_vector_column(engine, cast(Table, target_table.__table__))
+    vector = resolve_backend(engine).fulltext_vector_column(
+        engine, cast(Table, target_table.__table__)
+    )
     query = func.plainto_tsquery("english", query_concept_name)
 
     stmt = stmt.where(vector.op("@@")(query))  # Hits the GIN index instantly
@@ -369,9 +355,7 @@ def q_concept_name_fulltext(
 
     if sort:
         stmt = stmt.order_by(
-            *_concept_match_order_terms(
-                name_expr, rank_expr=func.ts_rank(vector, query)
-            )
+            *_concept_match_order_terms(name_expr, rank_expr=func.ts_rank(vector, query))
         )
 
     return stmt
@@ -438,11 +422,11 @@ def q_all_predicates_with_ancestry() -> Select:
 
 
 def q_edges(
-    concept_ids: Union[Tuple[int, ...], int],
+    concept_ids: tuple[int, ...] | int,
     direction: Literal["in", "out"],
-    predicate_ids: Optional[frozenset[str]] = None,
+    predicate_ids: frozenset[str] | None = None,
     active_only: bool = False,
-    on: Optional[date] = None,
+    on: date | None = None,
     within_domain: bool = False,
 ) -> Select:
     """Query edges for a batch of concept IDs, in either direction.
@@ -517,9 +501,9 @@ def q_ancestors(concept_id: int) -> Select:
 
 
 def q_relationships(
-    subjects: Optional[tuple[int, ...]],
-    predicates: Optional[tuple[str, ...]],
-    objects: Optional[tuple[int, ...]],
+    subjects: tuple[int, ...] | None,
+    predicates: tuple[str, ...] | None,
+    objects: tuple[int, ...] | None,
 ) -> Select:
 
     stmt = select(
@@ -552,9 +536,7 @@ def q_entities(
     ).apply(select(Concept.concept_id))
 
 
-def q_concept_filtered(
-    vocabulary_id: Optional[str] = None, domain_id: Optional[str] = None
-) -> Select:
+def q_concept_filtered(vocabulary_id: str | None = None, domain_id: str | None = None) -> Select:
     """Helper query for selecting standard concepts filtered by vocab/domain."""
     return ConceptFilter(
         domains=(domain_id,) if domain_id else None,
@@ -570,9 +552,7 @@ def q_concept_synonym_filtered(concept_id: int) -> Select:
     return stmt.where(Concept_Synonym.concept_id == concept_id)
 
 
-def q_singletons(
-    *, vocabulary_id: Optional[str] = None, domain_id: Optional[str] = None
-) -> Select:
+def q_singletons(*, vocabulary_id: str | None = None, domain_id: str | None = None) -> Select:
     """
     Query for singleton concepts (no parents, no children).
     Uses NOT EXISTS subqueries on the ancestor table.
@@ -600,9 +580,7 @@ def q_singletons(
     )
 
 
-def q_roots(
-    *, vocabulary_id: Optional[str] = None, domain_id: Optional[str] = None
-) -> Select:
+def q_roots(*, vocabulary_id: str | None = None, domain_id: str | None = None) -> Select:
     """Query for root concepts (no parents)."""
     stmt = q_concept_filtered(vocabulary_id=vocabulary_id, domain_id=domain_id)
     return stmt.where(
@@ -617,9 +595,7 @@ def q_roots(
     )
 
 
-def q_leaves(
-    *, vocabulary_id: Optional[str] = None, domain_id: Optional[str] = None
-) -> Select:
+def q_leaves(*, vocabulary_id: str | None = None, domain_id: str | None = None) -> Select:
     """Query for leaf concepts (no children)."""
     stmt = q_concept_filtered(vocabulary_id=vocabulary_id, domain_id=domain_id)
     return stmt.where(
@@ -641,11 +617,7 @@ def q_concept_domain_ids() -> Select:
 
 def q_concept_vocabulary_ids() -> Select:
     """Query distinct vocabulary IDs."""
-    return (
-        select(Concept.vocabulary_id)
-        .distinct()
-        .where(Concept.vocabulary_id.is_not(None))
-    )
+    return select(Concept.vocabulary_id).distinct().where(Concept.vocabulary_id.is_not(None))
 
 
 def q_concept_potential_ancestor(child_id: int, parent_id: int) -> Select:
@@ -666,7 +638,7 @@ def q_concept_potential_ancestor(child_id: int, parent_id: int) -> Select:
 
 
 def q_concept_potential_ancestors_batch(
-    child_ids: Tuple[int, ...], parent_ids: Tuple[int, ...]
+    child_ids: tuple[int, ...], parent_ids: tuple[int, ...]
 ) -> Select:
     """
     Check which of several candidate parents are ancestors of one or more children.
@@ -691,7 +663,7 @@ def q_concept_potential_ancestors_batch(
     )
 
 
-def q_concept_num_ancestors(concept_ids: Tuple[int, ...]) -> Select:
+def q_concept_num_ancestors(concept_ids: tuple[int, ...]) -> Select:
     """
     Count the number of ancestors for each concept in the batch.
     """
@@ -710,7 +682,7 @@ def q_concept_num_ancestors(concept_ids: Tuple[int, ...]) -> Select:
     )
 
 
-def q_concept_num_descendants(concept_ids: Tuple[int, ...]) -> Select:
+def q_concept_num_descendants(concept_ids: tuple[int, ...]) -> Select:
     """
     Count the number of descendants for each concept in the batch.
     """
@@ -719,9 +691,7 @@ def q_concept_num_descendants(concept_ids: Tuple[int, ...]) -> Select:
             Concept.concept_id,
             func.count(Concept_Ancestor.descendant_concept_id).label("num_descendants"),
         )
-        .join(
-            Concept_Ancestor, Concept.concept_id == Concept_Ancestor.ancestor_concept_id
-        )
+        .join(Concept_Ancestor, Concept.concept_id == Concept_Ancestor.ancestor_concept_id)
         .where(Concept.concept_id.in_(concept_ids))
         .where(Concept_Ancestor.min_levels_of_separation > 0)
         .group_by(Concept.concept_id)
diff --git a/src/omop_graph/graph/scoring.py b/src/omop_graph/graph/scoring.py
index 338afc9..1f8cdcd 100644
--- a/src/omop_graph/graph/scoring.py
+++ b/src/omop_graph/graph/scoring.py
@@ -14,7 +14,7 @@
 import re
 from dataclasses import dataclass, field
 from difflib import SequenceMatcher
-from typing import TYPE_CHECKING, List, Optional, Tuple
+from typing import TYPE_CHECKING
 
 import numpy as np
 
@@ -23,9 +23,10 @@
 from omop_graph.graph.paths import StandardConcept
 
 if TYPE_CHECKING:
-    from omop_graph.graph.kg import KnowledgeGraph
     from omop_emb.utils.embedding_utils import NearestConceptMatch
 
+    from omop_graph.graph.kg import KnowledgeGraph
+
 logger = logging.getLogger(__name__)
 
 
@@ -51,7 +52,7 @@ class StandardConceptWithScore(StandardConcept):
     """
 
     total_score: float = field(compare=True, default=0.0)
-    embedding_score: Optional[float] = field(compare=False, default=None)
+    embedding_score: float | None = field(compare=False, default=None)
     relevance: float = field(compare=False, default=0.0)
     parsimony_penalty: float = field(compare=False, default=0.0)
     broadness_bonus: float = field(compare=False, default=0.0)
@@ -67,12 +68,12 @@ def __repr__(self) -> str:
     def from_standard_concept(
         cls,
         standard_concept: StandardConcept,
-        embedding_score: Optional[float],
+        embedding_score: float | None,
         relevance: float,
         parsimony_penalty: float,
         broadness_bonus: float,
         total_score: float,
-    ) -> "StandardConceptWithScore":
+    ) -> StandardConceptWithScore:
         """
         Factory method to promote a StandardConcept to a scored version.
         """
@@ -89,11 +90,9 @@ def from_standard_concept(
 def score_standard_concepts(
     text: str,
     standard_concepts: tuple[StandardConcept, ...],
-    kg: "KnowledgeGraph",
-    nearest_concept_matches: Optional[
-        Tuple[Tuple[NearestConceptMatch, ...], ...]
-    ] = None,
-) -> List[StandardConceptWithScore]:
+    kg: KnowledgeGraph,
+    nearest_concept_matches: tuple[tuple[NearestConceptMatch, ...], ...] | None = None,
+) -> list[StandardConceptWithScore]:
     """
     Attach scoring metrics to each standard concept.
 
@@ -118,8 +117,8 @@ def score_standard_concepts(
 
     Notes
     -----
-    Standard concept scoring is ONLY performed for concepts that were matched via the embedding resolver, 
-    and only if nearest_concept_matches are provided. Concepts matched via non-embedding resolvers will still 
+    Standard concept scoring is ONLY performed for concepts that were matched via the embedding resolver,
+    and only if nearest_concept_matches are provided. Concepts matched via non-embedding resolvers will still
     receive parsimony and broadness scoring, but their relevance will be based solely on textual similarity.
 
     Returns
@@ -163,11 +162,11 @@ def score_standard_concepts(
 
 
 def _score_standard_concept(
-    kg: "KnowledgeGraph",
+    kg: KnowledgeGraph,
     text: str,
     standard_concept: StandardConcept,
     num_ancestors: int,
-    similarity_score: Optional[float],
+    similarity_score: float | None,
     alpha: float = 0.05,
     beta: float = 0.01,
 ) -> StandardConceptWithScore:
@@ -261,7 +260,7 @@ def _textual_similarity_score(
     # 1. Tokenize (keep standard normalization)
     stop_words = {"of", "the", "in", "and", "or", "to", "nos", "a", "an"}
 
-    def tokenize(text: str) -> List[str]:
+    def tokenize(text: str) -> list[str]:
         tokens = re.findall(r"\w+", text.lower())
         return [t for t in tokens if t not in stop_words]
 
diff --git a/src/omop_graph/graph/traverse.py b/src/omop_graph/graph/traverse.py
index efa995b..2e64830 100644
--- a/src/omop_graph/graph/traverse.py
+++ b/src/omop_graph/graph/traverse.py
@@ -12,9 +12,10 @@
 from __future__ import annotations
 
 from collections import deque
+from collections.abc import Iterable
 from dataclasses import dataclass
 from datetime import date
-from typing import TYPE_CHECKING, Iterable, List, Optional, Set, Tuple
+from typing import TYPE_CHECKING
 
 # Local Application Imports
 from omop_graph.extensions.omop_alchemy import PredicateKind
@@ -79,10 +80,10 @@ class GraphTrace:
     """
 
     seeds: tuple[int, ...]
-    steps: List[TraceStep]
-    terminated_reason: Optional[str] = None
+    steps: list[TraceStep]
+    terminated_reason: str | None = None
 
-    def summary(self, kg: "KnowledgeGraph", max_steps: int = 10) -> str:
+    def summary(self, kg: KnowledgeGraph, max_steps: int = 10) -> str:
         """
         Generate a human-readable summary of the traversal.
 
@@ -112,14 +113,14 @@ def summary(self, kg: "KnowledgeGraph", max_steps: int = 10) -> str:
 
 
 def traverse(
-    kg: "KnowledgeGraph",
+    kg: KnowledgeGraph,
     seeds: Iterable[int],
-    predicate_kinds: Optional[Set[PredicateKind]],
+    predicate_kinds: set[PredicateKind] | None,
     max_depth: int,
-    on: Optional[date],
-    max_nodes: Optional[int],
+    on: date | None,
+    max_nodes: int | None,
     trace: bool,
-) -> Tuple[Subgraph, Optional[GraphTrace]]:
+) -> tuple[Subgraph, GraphTrace | None]:
     """
     Perform a Breadth-First Search (BFS) traversal starting from seed nodes.
 
@@ -150,8 +151,8 @@ def traverse(
     unique_seeds = tuple(dict.fromkeys(seeds))
 
     visited = set()
-    edges_out: List[EdgeView] = []
-    trace_steps: List[TraceStep] = []
+    edges_out: list[EdgeView] = []
+    trace_steps: list[TraceStep] = []
 
     # Queue stores (concept_id, depth)
     q = deque((s, 0) for s in unique_seeds)
@@ -172,7 +173,7 @@ def traverse(
         if depth >= max_depth:
             continue
 
-        expanded: List[EdgeView] = []
+        expanded: list[EdgeView] = []
 
         # Iterate over outgoing edges
         with kg.session_factory() as session:
@@ -180,9 +181,7 @@ def traverse(
                 session=session,
                 concept_ids=node,
                 direction="out",
-                predicate_kinds=frozenset(predicate_kinds)
-                if predicate_kinds is not None
-                else None,
+                predicate_kinds=frozenset(predicate_kinds) if predicate_kinds is not None else None,
                 active_only=True,
                 on=on,
             ):
@@ -196,18 +195,14 @@ def traverse(
                     q.append((nxt, depth + 1))
 
         if trace:
-            trace_steps.append(
-                TraceStep(depth=depth, node=node, expanded_edges=tuple(expanded))
-            )
+            trace_steps.append(TraceStep(depth=depth, node=node, expanded_edges=tuple(expanded)))
 
     # Deduplicate edges and drop any that target an unvisited node.
     # The latter can happen when max_nodes terminates the loop while neighbour
     # nodes are still queued but never expanded, which would break the invariant
     # that every edge in the Subgraph is closed over its node set.
     dedup = {
-        (e.subject_id, e.predicate_id, e.object_id): e
-        for e in edges_out
-        if e.object_id in visited
+        (e.subject_id, e.predicate_id, e.object_id): e for e in edges_out if e.object_id in visited
     }
     sg = Subgraph(nodes=frozenset(visited), edges=tuple(dedup.values()))
 
diff --git a/src/omop_graph/oaklib_interface/omop_implementation.py b/src/omop_graph/oaklib_interface/omop_implementation.py
index f3fc47e..aff809c 100644
--- a/src/omop_graph/oaklib_interface/omop_implementation.py
+++ b/src/omop_graph/oaklib_interface/omop_implementation.py
@@ -1,11 +1,11 @@
 import logging
 from collections import defaultdict
-from typing import ClassVar, Dict, Iterable, Iterator, List, Optional, Tuple
+from collections.abc import Iterable, Iterator
+from typing import ClassVar
 
 import numpy as np
-from omop_alchemy.cdm.query import ConceptFilter
-
 from linkml_runtime.linkml_model.annotations import Annotation
+from oa_configurator import ResolvedCDMDatabase
 from oaklib.datamodels.search import (
     SearchConfiguration,
     SearchProperty,
@@ -27,19 +27,19 @@
     BasicOntologyInterface,
 )
 from oaklib.interfaces.text_annotator_interface import nen_annotation
+from oaklib.resource import OntologyResource
 from oaklib.types import CURIE, PRED_CURIE
+from omop_alchemy.cdm.query import ConceptFilter
+from sqlalchemy.exc import NoResultFound
 
-from omop_graph.graph import KnowledgeGraph, KnowledgeGraphEmbeddingConfiguration
+from omop_graph.db.session import cdm_session_factory, resolve_cdm_database
 from omop_graph.extensions.omop_alchemy import PredicateKind
+from omop_graph.graph import KnowledgeGraph, KnowledgeGraphEmbeddingConfiguration
 from omop_graph.graph.nodes import LabelMatchKind
+from omop_graph.oaklib_interface.omop_resource import OMOPOntologyResource
 from omop_graph.reasoning.grounding import GroundingConstraints, ground_term
 from omop_graph.reasoning.resolvers.resolver_pipeline import ResolverPipeline
 from omop_graph.render import bind_default_renderers
-from omop_graph.oaklib_interface.omop_resource import OMOPOntologyResource
-from omop_graph.db.session import cdm_session_factory, resolve_cdm_database
-
-from oa_configurator import ResolvedCDMDatabase
-from oaklib.resource import OntologyResource
 
 logger = logging.getLogger(__name__)
 
@@ -73,10 +73,7 @@ def _normalise_properties(config: SearchConfiguration) -> list[str]:
     list[str]
         A list of property strings.
     """
-    return [
-        getattr(p, "text", None) or str(p)
-        for p in (config.properties or [])  # type: ignore[union-attr]
-    ]
+    return [getattr(p, "text", None) or str(p) for p in (config.properties or [])]
 
 
 class OMOPBaseInterface:
@@ -93,7 +90,7 @@ def __init__(self, kg: KnowledgeGraph, **kwargs):
         super().__init__(**kwargs)
         self.kg = kg
 
-    def prefix_map(self) -> Dict[str, str]:
+    def prefix_map(self) -> dict[str, str]:
         """
         Return the prefix map for OMOP entities.
 
@@ -213,9 +210,9 @@ def __init__(self, kg: KnowledgeGraph, **kwargs):
     def annotate_text(
         self,
         text: str,
-        configuration: Optional[TextAnnotationConfiguration] = None,
-        query_embedding: Optional[np.ndarray] = None,
-        annotations: Optional[Dict[str, Annotation]] = None,
+        configuration: TextAnnotationConfiguration | None = None,
+        query_embedding: np.ndarray | None = None,
+        annotations: dict[str, Annotation] | None = None,
         max_depth: int = 6,
     ) -> Iterator[TextAnnotation]:
         """
@@ -253,20 +250,14 @@ def annotate_text(
 
         if isinstance(text, str) and configuration.token_exclusion_list:
             text = " ".join(
-                [
-                    term
-                    for term in text.split()
-                    if term not in configuration.token_exclusion_list
-                ]
+                [term for term in text.split() if term not in configuration.token_exclusion_list]
             )
         elif isinstance(text, tuple) and configuration.token_exclusion_list:
-            filtered_text = tuple()
+            filtered_text = ()
             # text is a tuple of string(s)
             for token in text:
                 filtered_text = filtered_text + tuple(
-                    term
-                    for term in token.split()
-                    if term not in configuration.token_exclusion_list
+                    term for term in token.split() if term not in configuration.token_exclusion_list
                 )
             text = " ".join(filtered_text)
 
@@ -274,27 +265,19 @@ def annotate_text(
 
             def split_annotations(ann):
                 if ann is not None and isinstance(ann.value, str):
-                    return tuple(
-                        val.strip() for val in ann.value.split(ANNOTATIONS_SPLIT_CHAR)
-                    )
+                    return tuple(val.strip() for val in ann.value.split(ANNOTATIONS_SPLIT_CHAR))
                 return None
 
             try:
-                parent_ids = split_annotations(
-                    annotations.get(ANNOTATIONS_PARENT_ID_KEY, None)
-                )
-                vocabs = split_annotations(
-                    annotations.get(ANNOTATIONS_VOCABS_KEY, None)
-                )
-                domains = split_annotations(
-                    annotations.get(ANNOTATIONS_DOMAINS_KEY, None)
-                )
-            except Exception as e:
+                parent_ids = split_annotations(annotations.get(ANNOTATIONS_PARENT_ID_KEY, None))
+                vocabs = split_annotations(annotations.get(ANNOTATIONS_VOCABS_KEY, None))
+                domains = split_annotations(annotations.get(ANNOTATIONS_DOMAINS_KEY, None))
+            except Exception:
                 logger.error(
                     f"Annotations passed in wrong format. Expected key: val, "
                     f"where val is separated by '{ANNOTATIONS_SPLIT_CHAR}' if using multiple values."
                 )
-                raise e
+                raise
         else:
             parent_ids = None
             vocabs = None
@@ -302,9 +285,7 @@ def split_annotations(ann):
 
         constraints = GroundingConstraints(
             parent_ids=(
-                tuple(self._parse_concept(pid) for pid in parent_ids)
-                if parent_ids
-                else None
+                tuple(self._parse_concept(pid) for pid in parent_ids) if parent_ids else None
             ),
             search_constraint=ConceptFilter(
                 domains=domains,
@@ -347,7 +328,7 @@ def __init__(self, *args, kg: KnowledgeGraph, **kwargs):
     def basic_search(
         self,
         search_term: str,
-        config: Optional[SearchConfiguration] = None,
+        config: SearchConfiguration | None = None,
     ) -> Iterable[CURIE]:
         """
         Perform a basic search over the knowledge graph.
@@ -416,16 +397,15 @@ def basic_search(
         if (
             SearchProperty.IDENTIFIER.text in props
             or SearchProperty.MAPPED_IDENTIFIER.text in props
-        ):
-            if ":" in search_term:
-                vocab, code = search_term.split(":", 1)
-                try:
-                    cid = self.kg.concept_id_by_code(vocab, code)
-                    if cid not in seen:
-                        seen.add(cid)
-                        yield self._concept_curie(cid)
-                except Exception:
-                    pass
+        ) and ":" in search_term:
+            vocab, code = search_term.split(":", 1)
+            try:
+                cid = self.kg.concept_id_by_code(vocab, code)
+                if cid not in seen:
+                    seen.add(cid)
+                    yield self._concept_curie(cid)
+            except NoResultFound:
+                pass
 
 
 class OMOPRelationGraphInterface(OMOPBaseInterface, BasicOntologyInterface):
@@ -442,7 +422,7 @@ def __init__(self, *args, kg: KnowledgeGraph, **kwargs):
     def supports_reasoning(self) -> bool:
         return False
 
-    def entity_aliases(self, curie: CURIE) -> List[str]:
+    def entity_aliases(self, curie: CURIE) -> list[str]:
         """
         Retrieve aliases (synonyms and codes) for a given entity.
 
@@ -467,7 +447,7 @@ def entity_aliases(self, curie: CURIE) -> List[str]:
         # vocabulary-qualified code alias
         aliases.add(f"{cv.vocabulary_id}:{cv.concept_code}")
 
-        return list(sorted(aliases))
+        return sorted(aliases)
 
     def parents(self, curie: CURIE) -> Iterable[CURIE]:
         """
@@ -486,7 +466,7 @@ def children(self, curie: CURIE) -> Iterable[CURIE]:
             yield self._concept_curie(child_id)
 
     @property
-    def default_language(self) -> Optional[str]:
+    def default_language(self) -> str | None:
         return "en"
 
     def ontologies(self) -> Iterable[CURIE]:
@@ -563,7 +543,7 @@ def roots(
 
     def leafs(
         self,
-        predicates: Optional[List[PRED_CURIE]] = None,
+        predicates: list[PRED_CURIE] | None = None,
         ignore_owl_nothing=True,
         filter_obsoletes=True,
     ) -> Iterable[CURIE]:
@@ -574,7 +554,7 @@ def leafs(
             yield self._concept_curie(cid)
 
     def singletons(
-        self, predicates: Optional[List[PRED_CURIE]] = None, filter_obsoletes=True
+        self, predicates: list[PRED_CURIE] | None = None, filter_obsoletes=True
     ) -> Iterable[CURIE]:
         """
         Retrieve singleton nodes (no parents or children).
@@ -582,14 +562,14 @@ def singletons(
         for cid in self.kg.singletons():
             yield self._concept_curie(cid)
 
-    def label(self, curie: CURIE, lang: Optional[str] = None) -> Optional[str]:
+    def label(self, curie: CURIE, lang: str | None = None) -> str | None:
         """
         Get the preferred label (concept_name) for a CURIE.
         """
         concept_id = self._parse_concept(curie)
         return self.kg.concept_view(concept_id).concept_name
 
-    def curies_by_label(self, label: str) -> List[CURIE]:
+    def curies_by_label(self, label: str) -> list[CURIE]:
         """
         Retrieve CURIEs that match the exact label.
         """
@@ -607,7 +587,7 @@ def relationships(  # ty: ignore[invalid-method-override]
         objects: Iterable[CURIE] | None = None,
         invert: bool = False,
         **kwargs,
-    ) -> Iterable[Tuple[CURIE, PRED_CURIE, CURIE]]:
+    ) -> Iterable[tuple[CURIE, PRED_CURIE, CURIE]]:
         """
         Query relationships between concepts.
 
@@ -629,15 +609,11 @@ def relationships(  # ty: ignore[invalid-method-override]
         """
 
         subject_ids = (
-            tuple([self._parse_concept(s) for s in subjects])
-            if subjects is not None
-            else None
+            tuple([self._parse_concept(s) for s in subjects]) if subjects is not None else None
         )
         predicate_ids = tuple(predicates) if predicates is not None else None
         object_ids = (
-            tuple([self._parse_concept(o) for o in objects])
-            if objects is not None
-            else None
+            tuple([self._parse_concept(o) for o in objects]) if objects is not None else None
         )
 
         relationships = tuple(
@@ -656,7 +632,7 @@ def relationships(  # ty: ignore[invalid-method-override]
                 self._concept_curie(o),
             )
 
-    def hierarchical_parents(self, curie: CURIE, isa_only: bool = False) -> List[CURIE]:
+    def hierarchical_parents(self, curie: CURIE, isa_only: bool = False) -> list[CURIE]:
         """
         Get hierarchical parents.
         """
@@ -726,7 +702,7 @@ def entailed_outgoing_relationships(
         self,
         curie: CURIE,
         predicates: list[PRED_CURIE] | None = None,
-    ) -> Iterable[Tuple[PRED_CURIE, CURIE]]:
+    ) -> Iterable[tuple[PRED_CURIE, CURIE]]:
         """
         Retrieve outgoing relationships, including those implied by the hierarchy.
 
@@ -736,9 +712,7 @@ def entailed_outgoing_relationships(
         raise NotImplementedError("Changes to the CDM currently prevents this function")
         concept_id = self._parse_concept(curie)
 
-        pred_filter = (
-            {self._parse_predicate(p) for p in predicates} if predicates else None
-        )
+        pred_filter = {self._parse_predicate(p) for p in predicates} if predicates else None
 
         with self.kg.session_factory() as session:
             for edge in self.kg.iter_edges(
@@ -761,25 +735,21 @@ def entailed_outgoing_relationships(
 
     def entailed_outputgoing_relationships_by_curie(
         self, *args, **kwargs
-    ) -> Iterable[Tuple[PRED_CURIE, CURIE]]:
+    ) -> Iterable[tuple[PRED_CURIE, CURIE]]:
         # return self.entailed_outgoing_relationships(*args, **kwargs)
-        raise NotImplementedError(
-            "Not Implemented: use entailed_outgoing_relationships instead"
-        )
+        raise NotImplementedError("Not Implemented: use entailed_outgoing_relationships instead")
 
     def entailed_incoming_relationships(
         self,
         curie: CURIE,
         predicates: list[PRED_CURIE] | None = None,
-    ) -> Iterable[Tuple[PRED_CURIE, CURIE]]:
+    ) -> Iterable[tuple[PRED_CURIE, CURIE]]:
         """
         Retrieve incoming relationships.
         """
         concept_id = self._parse_concept(curie)
 
-        pred_filter = (
-            {self._parse_predicate(p) for p in predicates} if predicates else None
-        )
+        pred_filter = {self._parse_predicate(p) for p in predicates} if predicates else None
 
         with self.kg.session_factory() as session:
             for edge in self.kg.iter_edges(
@@ -795,11 +765,9 @@ def entailed_incoming_relationships(
 
     def entailed_incoming_relationships_by_curie(
         self, *args, **kwargs
-    ) -> Iterable[Tuple[PRED_CURIE, CURIE]]:
+    ) -> Iterable[tuple[PRED_CURIE, CURIE]]:
         # return self.entailed_incoming_relationships(*args, **kwargs)
-        raise NotImplementedError(
-            "Not Implemented: use entailed_incoming_relationships instead"
-        )
+        raise NotImplementedError("Not Implemented: use entailed_incoming_relationships instead")
 
     def entailed_relationships_between(
         self,
@@ -812,9 +780,7 @@ def entailed_relationships_between(
         Currently disabled: always raises ``NotImplementedError``, per the
         raise message below, since a CDM change broke the body's assumptions.
         """
-        raise NotImplementedError(
-            "Change in OMOP CDM made this function not work anymore"
-        )
+        raise NotImplementedError("Change in OMOP CDM made this function not work anymore")
 
         subj_id = self._parse_concept(subject)
         obj_id = self._parse_concept(object)
@@ -830,7 +796,7 @@ def entailed_relationships_between(
             yield self._predicate_curie("is_a")
 
 
-class OMOPAlchemyImplementation(  # type: ignore[override]
+class OMOPAlchemyImplementation(
     OMOPRelationGraphInterface,
     OMOPSearchInterface,
     OMOPTextAnnotatorInterface,
@@ -877,7 +843,7 @@ def __init__(
         *,
         resolved: ResolvedCDMDatabase | None = None,
         kg: KnowledgeGraph | None = None,
-        kg_emb_config: Optional[KnowledgeGraphEmbeddingConfiguration] = None,
+        kg_emb_config: KnowledgeGraphEmbeddingConfiguration | None = None,
         **kwargs,
     ):
         if resource is None:
@@ -906,28 +872,22 @@ def _all_entailed_relationships(self):
 
     @property
     def edge_index(self):
-        raise NotImplementedError(
-            "edge_index is not supported for OMOP-backed adapters"
-        )
+        raise NotImplementedError("edge_index is not supported for OMOP-backed adapters")
 
     @property
     def entailed_edge_index(self):
-        raise NotImplementedError(
-            "entailed_edge_index is not supported for OMOP-backed adapters"
-        )
+        raise NotImplementedError("entailed_edge_index is not supported for OMOP-backed adapters")
 
     def obsoletes(self, include_merged=True) -> Iterable[CURIE]:
-        raise NotImplementedError(
-            "OMOP does not support OWL-style obsolescence semantics"
-        )
+        raise NotImplementedError("OMOP does not support OWL-style obsolescence semantics")
 
-    def dangling(self, curies: Optional[Iterable[CURIE]] = None) -> Iterable[CURIE]:
+    def dangling(self, curies: Iterable[CURIE] | None = None) -> Iterable[CURIE]:
         raise NotImplementedError("OMOP does not support dangling term semantics")
 
     def ontology_versions(self, ontology: CURIE) -> Iterable[str]:
         raise NotImplementedError("OMOP does not support ontology version semantics")
 
-    def ontology_metadata_map(self, ontology: CURIE) -> Dict[PRED_CURIE, List[str]]:
+    def ontology_metadata_map(self, ontology: CURIE) -> dict[PRED_CURIE, list[str]]:
         raise NotImplementedError("OMOP does not expose ontology-level metadata")
 
     def subsets(self) -> Iterable[CURIE]:
@@ -964,7 +924,7 @@ def save(self):
     def clone(self, resource):
         raise NotImplementedError("Cloning is not supported for OMOP adapters")
 
-    def definition(self, curie: CURIE, lang: Optional[str] = None) -> Optional[str]:
+    def definition(self, curie: CURIE, lang: str | None = None) -> str | None:
         raise NotImplementedError("OMOP does not support term definitions")
 
     def comments(self, curies, allow_none=True, lang=None):
diff --git a/src/omop_graph/oaklib_interface/omop_resource.py b/src/omop_graph/oaklib_interface/omop_resource.py
index e1b1238..99ea301 100644
--- a/src/omop_graph/oaklib_interface/omop_resource.py
+++ b/src/omop_graph/oaklib_interface/omop_resource.py
@@ -1,5 +1,4 @@
 from dataclasses import dataclass
-from typing import Optional
 
 from oaklib.resource import OntologyResource
 
@@ -22,9 +21,8 @@ class OMOPOntologyResource(OntologyResource):
         Whether the resource is read-only. Defaults to True.
     """
 
-    slug: Optional[str] = None  # type: ignore[assignment]
-    scheme: str = "omop"  # type: ignore[assignment]
-    readonly: bool = True  # type: ignore[assignment]
+    scheme: str | None = "omop"
+    readonly: bool | None = True
 
     def valid(self) -> bool:
         """
diff --git a/src/omop_graph/reasoning/concept_handlers/concept_helpers.py b/src/omop_graph/reasoning/concept_handlers/concept_helpers.py
index 6a5381d..85a9fe0 100644
--- a/src/omop_graph/reasoning/concept_handlers/concept_helpers.py
+++ b/src/omop_graph/reasoning/concept_handlers/concept_helpers.py
@@ -5,16 +5,14 @@
 concepts to their standard counterparts) using the Knowledge Graph.
 """
 
-from typing import Dict, Set
-
 # Local Application Imports
 from omop_graph.graph.kg import KnowledgeGraph
 
 
 def standardise_ids(
-    ids: Set[int],
+    ids: set[int],
     kg: KnowledgeGraph,
-) -> Dict[int, int]:
+) -> dict[int, int]:
     """
     Map a set of concept IDs to their Standard Concept IDs.
 
@@ -40,7 +38,7 @@ def standardise_ids(
     dict[int, int]
         A dictionary mapping the original input ID to its standardized ID.
     """
-    mapping: Dict[int, int] = {}
+    mapping: dict[int, int] = {}
 
     raise NotImplementedError(
         "predicate search has changed. Needs to change here too. Subsumes no longer valid."
diff --git a/src/omop_graph/reasoning/grounding.py b/src/omop_graph/reasoning/grounding.py
index 2daa409..606b449 100644
--- a/src/omop_graph/reasoning/grounding.py
+++ b/src/omop_graph/reasoning/grounding.py
@@ -17,31 +17,27 @@
 
 import logging
 from dataclasses import dataclass
-from typing import List, Optional, Tuple
 
 import numpy as np
 from omop_alchemy.cdm.query import ConceptFilter
 
+from omop_graph.extensions.emb import (
+    HAS_OMOP_EMB,
+    semantic_similarity,
+    try_get_embedding_writer_interface,
+)
 from omop_graph.extensions.omop_alchemy import PredicateKind
 from omop_graph.graph.kg import KnowledgeGraph
+from omop_graph.graph.nodes import LabelMatchKind
 from omop_graph.graph.paths import (
     StandardConcept,
     find_standard_paths,
 )
-from omop_graph.graph.scoring import (
-    StandardConceptWithScore, 
-    score_standard_concepts
-)
+from omop_graph.graph.scoring import StandardConceptWithScore, score_standard_concepts
 from omop_graph.reasoning.resolvers import (
     CandidateHit,
-    ResolverPipeline,
     EmbeddingResolver,
-)
-from omop_graph.graph.nodes import LabelMatchKind
-from omop_graph.extensions.emb import (
-    try_get_embedding_writer_interface,
-    semantic_similarity,
-    HAS_OMOP_EMB,
+    ResolverPipeline,
 )
 
 logger = logging.getLogger(__name__)
@@ -72,8 +68,8 @@ class GroundingConstraints:
         expanding candidates to unrelated concepts and diluting grounding results.
     """
 
-    parent_ids: Optional[Tuple[int, ...]]
-    search_constraint: Optional[ConceptFilter]
+    parent_ids: tuple[int, ...] | None
+    search_constraint: ConceptFilter | None
     max_depth: int = 6
     predicate_kinds: frozenset[PredicateKind] = frozenset({PredicateKind.IDENTITY})
 
@@ -85,7 +81,7 @@ def __post_init__(self) -> None:
             )
 
 
-def _query_text_with_context(query: str, context: Optional[str]) -> str:
+def _query_text_with_context(query: str, context: str | None) -> str:
     """Fold optional free-form context into the text used for on-demand query embedding.
 
     Notes
@@ -114,11 +110,11 @@ def ground_term(
     resolver_pipeline: ResolverPipeline,
     kg: KnowledgeGraph,
     query: str,
-    query_embedding: Optional[np.ndarray],
+    query_embedding: np.ndarray | None,
     constraints: GroundingConstraints,
-    max_candidates: Optional[int] = None,
-    context: Optional[str] = None,
-) -> List[StandardConceptWithScore]:
+    max_candidates: int | None = None,
+    context: str | None = None,
+) -> list[StandardConceptWithScore]:
     """
     Ground a text string to a ranked list of standard OMOP concepts.
 
@@ -146,7 +142,7 @@ def ground_term(
     list[StandardConceptWithScore]
         A list of standard concepts sorted by their total score (descending).
     """
-    standard_concepts: List[StandardConcept] = []
+    standard_concepts: list[StandardConcept] = []
 
     search_constraints = constraints.search_constraint
     if search_constraints is not None:
@@ -154,7 +150,9 @@ def ground_term(
 
     # Only do on demand embedding calculation if available and needed (having a EmbeddingResolver).
     # Falls back to None to disable embedding-based features if not available or not required.
-    require_embedding = HAS_OMOP_EMB and any(isinstance(resolver, EmbeddingResolver) for resolver in resolver_pipeline.resolvers)
+    require_embedding = HAS_OMOP_EMB and any(
+        isinstance(resolver, EmbeddingResolver) for resolver in resolver_pipeline.resolvers
+    )
 
     if query_embedding is None and require_embedding:
         embedding_writer = try_get_embedding_writer_interface(kg)
@@ -218,11 +216,9 @@ def ground_term(
         standard_concepts.extend(candidate_standard_concepts)
 
     if not standard_concepts:
-        logger.info(
-            f"No standard concepts found for '{query}' after hierarchy validation."
-        )
+        logger.info(f"No standard concepts found for '{query}' after hierarchy validation.")
         return []
-    
+
     # Only calculate nearest concept matches for the embedding resolver matches
     # split is done that semantic similarity has less concepts to search for
     matched_standard_concepts_for_embedding = [
@@ -230,13 +226,13 @@ def ground_term(
     ]
     if matched_standard_concepts_for_embedding:
         if query_embedding is None:
-            raise RuntimeError("Query embedding cannot be None if the EmbeddingResolver is used and returned matches.")
-        nearest_concept_matches_for_standard_embedding_concepts = (
-            semantic_similarity(
-                kg=kg, 
-                standard_concepts=matched_standard_concepts_for_embedding, 
-                query_embedding=query_embedding
+            raise RuntimeError(
+                "Query embedding cannot be None if the EmbeddingResolver is used and returned matches."
             )
+        nearest_concept_matches_for_standard_embedding_concepts = semantic_similarity(
+            kg=kg,
+            standard_concepts=matched_standard_concepts_for_embedding,
+            query_embedding=query_embedding,
         )
         embedding_standard_concepts_with_score = score_standard_concepts(
             text=query,
@@ -247,16 +243,20 @@ def ground_term(
     else:
         embedding_standard_concepts_with_score = []
 
-    # Score the other standard concepts that were not matched via embedding resolver 
+    # Score the other standard concepts that were not matched via embedding resolver
     non_embedding_standard_concepts_with_score = score_standard_concepts(
         text=query,
-        standard_concepts=tuple(sc for sc in standard_concepts if sc.match_kind != LabelMatchKind.EMBEDDING),
+        standard_concepts=tuple(
+            sc for sc in standard_concepts if sc.match_kind != LabelMatchKind.EMBEDDING
+        ),
         kg=kg,
         nearest_concept_matches=None,  # No embedding-based scoring for non-embedding matches
     )
 
     # combine the two different ones
-    standard_concepts_with_score = embedding_standard_concepts_with_score + non_embedding_standard_concepts_with_score
+    standard_concepts_with_score = (
+        embedding_standard_concepts_with_score + non_embedding_standard_concepts_with_score
+    )
 
     best_by_concept_id: dict[int, StandardConceptWithScore] = {}
     for concept in standard_concepts_with_score:
@@ -267,21 +267,17 @@ def ground_term(
     deduped_ranked = sorted(
         best_by_concept_id.values(), key=lambda sc: sc.total_score, reverse=True
     )
-    return (
-        deduped_ranked[:max_candidates]
-        if max_candidates is not None
-        else deduped_ranked
-    )
+    return deduped_ranked[:max_candidates] if max_candidates is not None else deduped_ranked
 
 
 def find_standard_concepts(
     kg: KnowledgeGraph,
     candidate: CandidateHit,
-    parent_ids: Optional[Tuple[int, ...]],
+    parent_ids: tuple[int, ...] | None,
     max_depth: int,
-    max_paths: Optional[int] = 3,
+    max_paths: int | None = 3,
     predicate_kinds: frozenset[PredicateKind] = frozenset({PredicateKind.IDENTITY}),
-) -> List[StandardConcept]:
+) -> list[StandardConcept]:
     """
     Identify standard concepts related to a candidate, optionally satisfying parent
     constraints.
diff --git a/src/omop_graph/reasoning/phenotypes/phenotype_simplifier.py b/src/omop_graph/reasoning/phenotypes/phenotype_simplifier.py
index b5830ef..fc4184a 100644
--- a/src/omop_graph/reasoning/phenotypes/phenotype_simplifier.py
+++ b/src/omop_graph/reasoning/phenotypes/phenotype_simplifier.py
@@ -1,7 +1,8 @@
-from dataclasses import dataclass, field
 from collections import defaultdict
-from typing import Dict, Set, List
+from dataclasses import dataclass, field
+
 from omop_graph.graph.kg import KnowledgeGraph
+
 from ..concept_handlers import standardise_ids
 
 
@@ -114,16 +115,12 @@ def find_common_parents(
         stats.purity = stats.coverage / denom if denom else 0.0
         stats.completeness = stats.coverage / max(len(seeds), 1)
 
-    return {
-        parent: stats
-        for parent, stats in final.items()
-        if stats.coverage >= min_coverage
-    }
+    return {parent: stats for parent, stats in final.items() if stats.coverage >= min_coverage}
 
 
 def greedy_parent_cover(
-    seeds: Set[int],
-    candidates: Dict[int, ParentStatistics],
+    seeds: set[int],
+    candidates: dict[int, ParentStatistics],
     *,
     target_coverage_ratio: float = 1.0,  # e.g. 0.95 for “good enough”
     alpha: float = 1.0,
@@ -131,9 +128,9 @@ def greedy_parent_cover(
     gamma: float = 0.3,
     delta: float = 0.7,
     min_gain: int = 1,
-) -> List[int]:
+) -> list[int]:
     remaining = set(seeds)
-    selected: List[int] = []
+    selected: list[int] = []
 
     def score(c: ParentStatistics, gain: int) -> float:
         if gain <= 0:
@@ -151,7 +148,7 @@ def score(c: ParentStatistics, gain: int) -> float:
 
         best_id = None
         best_score = -1.0
-        best_gain_set: Set[int] = set()
+        best_gain_set: set[int] = set()
 
         for cid, c in candidates.items():
             gain_set = c.found & remaining
diff --git a/src/omop_graph/reasoning/resolvers/__init__.py b/src/omop_graph/reasoning/resolvers/__init__.py
index d8492b4..fc80e3b 100644
--- a/src/omop_graph/reasoning/resolvers/__init__.py
+++ b/src/omop_graph/reasoning/resolvers/__init__.py
@@ -1,19 +1,19 @@
+from .resolver_pipeline import ResolverPipeline
 from .resolvers import (
     CandidateHit,
+    CandidateResolver,
+    EmbeddingResolver,
     ExactLabelResolver,
     ExactSynonymResolver,
     PartialLabelResolver,
-    CandidateResolver,
-    EmbeddingResolver,
 )
-from .resolver_pipeline import ResolverPipeline
 
 __all__ = [
     "CandidateHit",
+    "CandidateResolver",
+    "EmbeddingResolver",
     "ExactLabelResolver",
     "ExactSynonymResolver",
     "PartialLabelResolver",
-    "CandidateResolver",
     "ResolverPipeline",
-    "EmbeddingResolver",
 ]
diff --git a/src/omop_graph/reasoning/resolvers/resolver_pipeline.py b/src/omop_graph/reasoning/resolvers/resolver_pipeline.py
index cc8c3e3..c8098f1 100644
--- a/src/omop_graph/reasoning/resolvers/resolver_pipeline.py
+++ b/src/omop_graph/reasoning/resolvers/resolver_pipeline.py
@@ -9,7 +9,7 @@
 from __future__ import annotations
 
 import logging
-from typing import Generator, Optional, Tuple, Type
+from collections.abc import Generator
 
 from omop_alchemy.cdm.query import ConceptFilter
 
@@ -48,8 +48,8 @@ class ResolverPipeline:
 
     def __init__(
         self,
-        resolvers: Tuple[CandidateResolver, ...],
-        stop_after_resolver: Optional[str | Type[CandidateResolver]] = None,
+        resolvers: tuple[CandidateResolver, ...],
+        stop_after_resolver: str | type[CandidateResolver] | None = None,
     ):
         self._resolver_map = {type(r): r for r in resolvers}
 
@@ -62,11 +62,7 @@ def __init__(
             # If they passed a string name, find the matching type
             if isinstance(stop_after_resolver, str):
                 match = next(
-                    (
-                        t
-                        for t in self._resolver_map
-                        if t.__name__ == stop_after_resolver
-                    ),
+                    (t for t in self._resolver_map if t.__name__ == stop_after_resolver),
                     None,
                 )
                 if not match:
@@ -83,11 +79,11 @@ def __repr__(self) -> str:
         return f"ResolverPipeline(resolvers={resolver_names}, stop_after_resolver={self.stop_at.__name__ if self.stop_at else None})"
 
     @property
-    def resolvers(self) -> Tuple[CandidateResolver, ...]:
+    def resolvers(self) -> tuple[CandidateResolver, ...]:
         return self._resolvers
 
     @property
-    def stop_at(self) -> Optional[Type[CandidateResolver]]:
+    def stop_at(self) -> type[CandidateResolver] | None:
         """
         The class type of the resolver where the pipeline is configured to truncate.
         Returns None if the pipeline is configured to run to completion.
@@ -95,7 +91,7 @@ def stop_at(self) -> Optional[Type[CandidateResolver]]:
         return self._stop_at
 
     @classmethod
-    def with_all_resolvers(cls) -> "ResolverPipeline":
+    def with_all_resolvers(cls) -> ResolverPipeline:
         """
         Create a pipeline configured with all available resolvers.
 
@@ -111,7 +107,7 @@ def resolve(
         self,
         kg: KnowledgeGraph,
         query: str,
-        constraints: Optional[ConceptFilter] = None,
+        constraints: ConceptFilter | None = None,
         **kwargs,
     ) -> Generator[CandidateHit, None, None]:
         """
@@ -135,9 +131,7 @@ def resolve(
         seen = set()
 
         for resolver in self.resolvers:
-            hits = resolver.resolve(
-                kg=kg, query=query, constraints=constraints, **kwargs
-            )
+            hits = resolver.resolve(kg=kg, query=query, constraints=constraints, **kwargs)
 
             for hit in hits:
                 if hit.concept_id not in seen:
diff --git a/src/omop_graph/reasoning/resolvers/resolvers.py b/src/omop_graph/reasoning/resolvers/resolvers.py
index 564677a..defcbef 100644
--- a/src/omop_graph/reasoning/resolvers/resolvers.py
+++ b/src/omop_graph/reasoning/resolvers/resolvers.py
@@ -11,20 +11,21 @@
 
 from __future__ import annotations
 
-from dataclasses import dataclass
-from typing import TYPE_CHECKING, Iterable, Optional, Tuple
 import logging
-import numpy as np
+from collections.abc import Iterable
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
 
+import numpy as np
 from omop_alchemy.cdm.query import ConceptFilter
 
-from omop_graph.graph.kg import KnowledgeGraph
-from omop_graph.graph.nodes import LabelMatch, LabelMatchKind
 from omop_graph.extensions.emb import (
     HAS_OMOP_EMB,
     MissingExtensionError,
     get_neareast_concepts,
 )
+from omop_graph.graph.kg import KnowledgeGraph
+from omop_graph.graph.nodes import LabelMatch, LabelMatchKind
 
 if TYPE_CHECKING:
     from omop_graph.graph.kg import KnowledgeGraph
@@ -88,10 +89,10 @@ def get_matches(
         self,
         kg: KnowledgeGraph,
         query: str,
-        constraints: Optional[ConceptFilter] = None,
+        constraints: ConceptFilter | None = None,
         sort: bool = False,
         **kwargs,
-    ) -> Tuple[LabelMatch, ...]:
+    ) -> tuple[LabelMatch, ...]:
         """
         Execute the search strategy against the Knowledge Graph.
 
@@ -125,7 +126,7 @@ def resolve(
         self,
         kg: KnowledgeGraph,
         query: str,
-        constraints: Optional[ConceptFilter] = None,
+        constraints: ConceptFilter | None = None,
         **kwargs,
     ) -> Iterable[CandidateHit]:
         """
@@ -236,17 +237,17 @@ def get_matches(
         self,
         kg: KnowledgeGraph,
         query: str,
-        constraints: Optional[ConceptFilter] = None,
+        constraints: ConceptFilter | None = None,
         sort: bool = False,
-        query_embedding: Optional[np.ndarray] = None,
+        query_embedding: np.ndarray | None = None,
         **kwargs,
-    ) -> Tuple[LabelMatch, ...]:
+    ) -> tuple[LabelMatch, ...]:
         if not HAS_OMOP_EMB:
             raise MissingExtensionError("EmbeddingResolver")
 
         from omop_emb.utils.embedding_utils import EmbeddingConceptFilter
 
-        concept_filter: Optional[EmbeddingConceptFilter] = (
+        concept_filter: EmbeddingConceptFilter | None = (
             EmbeddingConceptFilter(
                 concept_ids=constraints.concept_ids,
                 domains=constraints.domains,
@@ -267,9 +268,7 @@ def get_matches(
             kg=kg,
             query_embedding=query_embedding,
             concept_filter=concept_filter,
-            k=constraints.limit
-            if isinstance(constraints, ConceptFilter)
-            else None,
+            k=constraints.limit if isinstance(constraints, ConceptFilter) else None,
         )
         if matches is None:
             return ()
@@ -300,7 +299,7 @@ def get_matches(
 
 
 # Default sequence of resolvers to be used in a pipeline
-ALL_RESOLVERS: Tuple[CandidateResolver, ...] = (
+ALL_RESOLVERS: tuple[CandidateResolver, ...] = (
     ExactLabelResolver(),
     ExactSynonymResolver(),
     PartialLabelResolver(),
diff --git a/src/omop_graph/render/__init__.py b/src/omop_graph/render/__init__.py
index 1d777b2..ce4a0d1 100644
--- a/src/omop_graph/render/__init__.py
+++ b/src/omop_graph/render/__init__.py
@@ -11,17 +11,17 @@
 """
 
 from .auto import (
+    bind_default_renderers,
+    render_explained_path,
+    render_path,
     render_subgraph,
     render_trace,
-    render_path,
-    render_explained_path,
-    bind_default_renderers,
 )
 
 __all__ = [
+    "bind_default_renderers",
+    "render_explained_path",
+    "render_path",
     "render_subgraph",
     "render_trace",
-    "render_path",
-    "render_explained_path",
-    "bind_default_renderers",
 ]
diff --git a/src/omop_graph/render/auto.py b/src/omop_graph/render/auto.py
index 3ee92ac..732f48d 100644
--- a/src/omop_graph/render/auto.py
+++ b/src/omop_graph/render/auto.py
@@ -1,38 +1,38 @@
 from __future__ import annotations
 
-from typing import Literal
+from collections.abc import Callable
+from typing import Any, Literal
 
 from omop_graph.graph.paths import GraphPath, PathExplanation
-from omop_graph.graph.traverse import Subgraph, GraphTrace
+from omop_graph.graph.traverse import GraphTrace, Subgraph
 
 from .html import (
+    explained_path_html,
+    path_html,
     subgraph_html,
     trace_html_with_cards,
-    path_html,
-    explained_path_html,
+)
+from .mmd import (
+    path_mermaid,
+    subgraph_mermaid,
 )
 from .text import (
+    explained_path_text,
+    path_text,
     subgraph_text,
     trace_text,
-    path_text,
-    explained_path_text,
 )
-from .mmd import (
-    subgraph_mermaid,
-    path_mermaid,
-)
-
 
 Format = Literal["auto", "html", "text", "mmd"]
 
 
 def _in_notebook() -> bool:
     try:
-        from IPython.core.getipython import get_ipython  # type: ignore[import-unresolved]
+        from IPython.core.getipython import get_ipython
 
         ip = get_ipython()
         return ip is not None and "IPKernelApp" in ip.config
-    except Exception:
+    except ImportError:
         return False
 
 
@@ -94,9 +94,14 @@ def render_explained_path(
     return explained_path_text(kg, explanation)
 
 
+def _install_repr_html(cls: type[Any], render: Callable[[Any], str]) -> None:
+    """Set render as the IPython rich-display hook ``_repr_html_`` of cls."""
+    cls._repr_html_ = render
+
+
 def bind_default_renderers(kg):
-    from omop_graph.render import render_subgraph, render_trace, render_path
+    from omop_graph.render import render_path, render_subgraph, render_trace
 
-    Subgraph._repr_html_ = lambda self: render_subgraph(kg, self, format="html")  # type: ignore
-    GraphTrace._repr_html_ = lambda self: render_trace(kg, self, format="html")  # type: ignore
-    GraphPath._repr_html_ = lambda self: render_path(kg, self, format="html")  # type: ignore
+    _install_repr_html(Subgraph, lambda self: render_subgraph(kg, self, format="html"))
+    _install_repr_html(GraphTrace, lambda self: render_trace(kg, self, format="html"))
+    _install_repr_html(GraphPath, lambda self: render_path(kg, self, format="html"))
diff --git a/src/omop_graph/render/html.py b/src/omop_graph/render/html.py
index 373c5d4..27801b6 100644
--- a/src/omop_graph/render/html.py
+++ b/src/omop_graph/render/html.py
@@ -3,8 +3,8 @@
 from html import escape
 
 from omop_graph.graph.nodes import ConceptView
-from omop_graph.graph.traverse import Subgraph, GraphTrace
 from omop_graph.graph.paths import GraphPath, PathExplanation
+from omop_graph.graph.traverse import GraphTrace, Subgraph
 
 
 def concept_card(c: ConceptView) -> str:
diff --git a/src/omop_graph/render/mmd.py b/src/omop_graph/render/mmd.py
index 268c590..36bb054 100644
--- a/src/omop_graph/render/mmd.py
+++ b/src/omop_graph/render/mmd.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
-from omop_graph.graph.traverse import Subgraph
 from omop_graph.graph.paths import GraphPath
+from omop_graph.graph.traverse import Subgraph
 
 
 def subgraph_mermaid(kg, sg: Subgraph) -> str:
diff --git a/src/omop_graph/render/text.py b/src/omop_graph/render/text.py
index 436723d..f283a81 100644
--- a/src/omop_graph/render/text.py
+++ b/src/omop_graph/render/text.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from omop_graph.graph.paths import GraphPath, PathExplanation
-from omop_graph.graph.traverse import Subgraph, GraphTrace
+from omop_graph.graph.traverse import GraphTrace, Subgraph
 
 
 def subgraph_text(kg, sg: Subgraph) -> str:
@@ -67,9 +67,7 @@ def explained_path_text(kg, explanation: PathExplanation) -> str:
     for s in explanation.steps:
         subj = kg.concept_view(s.step.subject.concept_id)
         obj = kg.concept_view(s.step.object.concept_id)
-        depth = (
-            f"depth={s.traversal_depth}" if s.traversal_depth is not None else "depth=?"
-        )
+        depth = f"depth={s.traversal_depth}" if s.traversal_depth is not None else "depth=?"
         lines.append(
             f"  {subj.concept_name} --[{s.step.predicate}]--> {obj.concept_name} "
             f"({s.predicate_kind.name}, {depth}) {s.reason}"
diff --git a/tests/conftest.py b/tests/conftest.py
index ed094b8..70c4ff2 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -7,11 +7,10 @@
 
 def _isolated_cdm_test_database(request, field_name, *, dialect=None):
     from oa_configurator.testing import isolated_test_database
+
     from omop_graph.config import OmopGraphConfig
 
-    return isolated_test_database(
-        OmopGraphConfig, field_name, dialect=dialect, request=request
-    )
+    return isolated_test_database(OmopGraphConfig, field_name, dialect=dialect, request=request)
 
 
 @pytest.fixture
diff --git a/tests/fixtures/helpers.py b/tests/fixtures/helpers.py
index 7de6513..fa1e6ea 100644
--- a/tests/fixtures/helpers.py
+++ b/tests/fixtures/helpers.py
@@ -2,22 +2,25 @@
 
 from __future__ import annotations
 
-from typing import cast
+from typing import Any
 
 import sqlalchemy as sa
-from omop_alchemy.cdm.model.vocabulary import (
-    Concept,
-    Concept_Class,
-    Domain,
-    Vocabulary
-)
-
-VOCAB_TABLES = cast(
-    "tuple[sa.Table, ...]",
-    (Domain.__table__, Vocabulary.__table__, Concept_Class.__table__, Concept.__table__),
-)
-"""Domain/Vocabulary/Concept_Class/Concept: the minimal vocab bootstrap set
-every split/non-split fixture needs, in FK dependency order. A consumer
-needing more tables (e.g. Relationship, Concept_Relationship) extends this
-tuple rather than redefining its own copy of the shared core.
-"""
+from oa_configurator import ResolvedCDMDatabase, ResolvedDatabase
+from omop_alchemy.cdm.model.vocabulary import Concept, Concept_Class, Domain, Vocabulary
+
+
+def require_cdm(resolved: ResolvedDatabase | None) -> ResolvedCDMDatabase:
+    """Narrow a resolved database to the CDM kind that the CDM session factory requires."""
+    assert isinstance(resolved, ResolvedCDMDatabase)
+    return resolved
+
+
+def table_of(model: Any) -> sa.Table:
+    """The Table a mapped class is bound to, narrowed from the declarative base's FromClause."""
+    table = model.__table__
+    assert isinstance(table, sa.Table)
+    return table
+
+
+# The minimal vocab bootstrap set every split/non-split fixture needs, in FK dependency order.
+VOCAB_TABLES = tuple(table_of(model) for model in (Domain, Vocabulary, Concept_Class, Concept))
diff --git a/tests/fixtures/mock_cdm.py b/tests/fixtures/mock_cdm.py
index 5752771..13c31ff 100644
--- a/tests/fixtures/mock_cdm.py
+++ b/tests/fixtures/mock_cdm.py
@@ -1,15 +1,12 @@
 from __future__ import annotations
 
+from collections.abc import Iterator
 from datetime import date
-from typing import Iterator, cast
+from typing import cast
 
 import pytest
 import sqlalchemy as sa
-from sqlalchemy.orm import Session, sessionmaker
-
 from oa_configurator.testing import isolated_test_database, scoped_test_schema
-from orm_loader.helpers import Base
-from orm_loader.backends import resolve_backend
 from omop_alchemy.cdm.model.vocabulary.concept import Concept
 from omop_alchemy.cdm.model.vocabulary.concept_ancestor import Concept_Ancestor
 from omop_alchemy.cdm.model.vocabulary.concept_class import Concept_Class
@@ -18,7 +15,11 @@
 from omop_alchemy.cdm.model.vocabulary.domain import Domain
 from omop_alchemy.cdm.model.vocabulary.relationship import Relationship
 from omop_alchemy.cdm.model.vocabulary.vocabulary import Vocabulary
+from orm_loader.backends import resolve_backend
+from orm_loader.helpers import Base
+from sqlalchemy.orm import Session, sessionmaker
 
+from fixtures.helpers import require_cdm
 from omop_graph.config import OmopGraphConfig
 from omop_graph.extensions.omop_alchemy import (
     PredicateKind,
@@ -34,10 +35,12 @@
 
 @pytest.fixture(scope="module")
 def mock_cdm_engine() -> Iterator[sa.Engine]:
-    with isolated_test_database(OmopGraphConfig, "test_cdm_db_pg") as db:
-        with scoped_test_schema(db.resolved, prefix="mock_cdm") as scoped:
-            _create_mock_cdm_tables(scoped.engine)
-            yield scoped.engine
+    with (
+        isolated_test_database(OmopGraphConfig, "test_cdm_db_pg") as db,
+        scoped_test_schema(require_cdm(db.resolved), prefix="mock_cdm") as scoped,
+    ):
+        _create_mock_cdm_tables(scoped.engine)
+        yield scoped.engine
 
 
 @pytest.fixture(scope="module")
@@ -93,17 +96,13 @@ def _create_mock_cdm_tables(engine: sa.Engine) -> None:
         ),
     ]
 )
-def mock_cdm_kg(
-    request: pytest.FixtureRequest, monkeypatch: pytest.MonkeyPatch
-) -> KnowledgeGraph:
+def mock_cdm_kg(request: pytest.FixtureRequest, monkeypatch: pytest.MonkeyPatch) -> KnowledgeGraph:
     # Grounding tests here focus on SQL + resolver + path pipeline.
     monkeypatch.setattr(
         "omop_graph.reasoning.grounding.try_get_embedding_writer_interface",
         lambda _kg: None,
     )
-    fixture_name = (
-        "mock_cdm_engine_sqlite" if request.param == "sqlite" else "mock_cdm_engine"
-    )
+    fixture_name = "mock_cdm_engine_sqlite" if request.param == "sqlite" else "mock_cdm_engine"
     engine = request.getfixturevalue(fixture_name)
     return KnowledgeGraph(sessionmaker(bind=engine))
 
@@ -412,7 +411,11 @@ def seed_relationship_vocabulary(engine: sa.Engine) -> None:
                             valid_start_date=date(2020, 1, 1),
                             valid_end_date=date(2099, 12, 31),
                         ),
-                        Domain(domain_id="Metadata", domain_name="Metadata", domain_concept_id=CONCEPT_META_ID),
+                        Domain(
+                            domain_id="Metadata",
+                            domain_name="Metadata",
+                            domain_concept_id=CONCEPT_META_ID,
+                        ),
                         Vocabulary(
                             vocabulary_id="OMOP",
                             vocabulary_name="OMOP",
diff --git a/tests/test_concept_queries.py b/tests/test_concept_queries.py
index cef9bc3..8d41eb7 100644
--- a/tests/test_concept_queries.py
+++ b/tests/test_concept_queries.py
@@ -2,19 +2,18 @@
 
 from __future__ import annotations
 
+from collections.abc import Iterator
 from datetime import date
-from typing import Iterator
 
 import pytest
 import sqlalchemy as sa
-from sqlalchemy.orm import Session
-
+from fixtures.helpers import VOCAB_TABLES
 from oa_configurator.testing import scoped_test_schema
-from orm_loader.helpers import Base
-from orm_loader.backends import resolve_backend
-
 from omop_alchemy.cdm.model.vocabulary import Concept, Concept_Class, Domain, Vocabulary
 from omop_alchemy.cdm.query import ConceptFilter
+from orm_loader.backends import resolve_backend
+from orm_loader.helpers import Base
+from sqlalchemy.orm import Session
 
 from omop_graph.graph.nodes import ConceptView
 from omop_graph.graph.queries import (
@@ -24,8 +23,6 @@
     q_entities,
 )
 
-from fixtures.helpers import VOCAB_TABLES
-
 _META_CONCEPT_ID = 0
 
 
@@ -72,8 +69,16 @@ def concept(
                             valid_start_date=valid_from,
                             valid_end_date=valid_until,
                         ),
-                        Domain(domain_id="Metadata", domain_name="Metadata", domain_concept_id=_META_CONCEPT_ID),
-                        Domain(domain_id="Condition", domain_name="Condition", domain_concept_id=_META_CONCEPT_ID),
+                        Domain(
+                            domain_id="Metadata",
+                            domain_name="Metadata",
+                            domain_concept_id=_META_CONCEPT_ID,
+                        ),
+                        Domain(
+                            domain_id="Condition",
+                            domain_name="Condition",
+                            domain_concept_id=_META_CONCEPT_ID,
+                        ),
                         Vocabulary(
                             vocabulary_id="OMOP",
                             vocabulary_name="OMOP",
@@ -153,9 +158,7 @@ def test_concept_views_use_canonical_standard_and_active_flags(
             view.concept_id: view
             for view in (
                 ConceptView.from_row(row)
-                for row in session.execute(
-                    q_concept_views((1, 2, 3, 4, 5), sort=False)
-                )
+                for row in session.execute(q_concept_views((1, 2, 3, 4, 5), sort=False))
             )
         }
 
@@ -188,9 +191,7 @@ def test_label_query_projects_and_orders_canonical_flags(
         rows = session.execute(q_concept_name_match("Shared label")).all()
 
     assert [row.concept_id for row in rows] == [1, 2, 4, 3, 5]
-    statuses = {
-        row.concept_id: (row.is_standard, row.is_active) for row in rows
-    }
+    statuses = {row.concept_id: (row.is_standard, row.is_active) for row in rows}
     assert statuses == {
         1: (True, True),
         2: (True, True),
diff --git a/tests/test_edges_cross_domain_regression.py b/tests/test_edges_cross_domain_regression.py
index 97c1435..230fc14 100644
--- a/tests/test_edges_cross_domain_regression.py
+++ b/tests/test_edges_cross_domain_regression.py
@@ -11,17 +11,17 @@
 from __future__ import annotations
 
 import csv
+from collections.abc import Iterator
 from datetime import date
-from importlib.resources import files
-from typing import Iterator, cast
+from importlib.resources import as_file, files
+from typing import cast
 
 import pytest
 import sqlalchemy as sa
-from sqlalchemy.orm import sessionmaker
-
-from orm_loader.helpers import Base
 from omop_alchemy.cdm.model.vocabulary.concept import Concept
 from omop_alchemy.cdm.model.vocabulary.concept_relationship import Concept_Relationship
+from orm_loader.helpers import Base
+from sqlalchemy.orm import sessionmaker
 
 from omop_graph.extensions.omop_alchemy import (
     PredicateKind,
@@ -114,7 +114,9 @@ def procedure_kg() -> Iterator[KnowledgeGraph]:
         session.add_all(
             RelationshipMapping(
                 relationship_id=relationship_id,
-                predicate_kind=PredicateKind.HIERARCHY if relationship_id == "Is a" else PredicateKind.ATTRIBUTE,
+                predicate_kind=PredicateKind.HIERARCHY
+                if relationship_id == "Is a"
+                else PredicateKind.ATTRIBUTE,
                 predicate_subkind=subkind,
             )
             for relationship_id, _, subkind in edges
@@ -127,7 +129,9 @@ def procedure_kg() -> Iterator[KnowledgeGraph]:
 
 
 def _attribute_pairs(edges) -> set[tuple[str, int]]:
-    return {(e.predicate_id, e.object_id) for e in edges if e.predicate_kind == PredicateKind.ATTRIBUTE}
+    return {
+        (e.predicate_id, e.object_id) for e in edges if e.predicate_kind == PredicateKind.ATTRIBUTE
+    }
 
 
 EXPECTED_ATTRIBUTE_PAIRS = {(rel, obj) for rel, obj, _ in ATTRIBUTE_EDGES}
@@ -140,7 +144,9 @@ def test_edges_include_cross_domain_attributes_by_default(procedure_kg: Knowledg
     assert ("Is a", PARENT_PROCEDURE) in {(e.predicate_id, e.object_id) for e in edges}
 
 
-def test_attribute_kind_filter_returns_cross_domain_attributes(procedure_kg: KnowledgeGraph) -> None:
+def test_attribute_kind_filter_returns_cross_domain_attributes(
+    procedure_kg: KnowledgeGraph,
+) -> None:
     edges = procedure_kg.edges(
         KNEE_REPLACEMENT,
         direction="out",
@@ -200,7 +206,7 @@ def test_traverse_reaches_cross_domain_attributes(procedure_kg: KnowledgeGraph)
 
 def test_shipped_mapping_classes_snomed_defining_attributes_as_attributes() -> None:
     mapping_file = files("omop_graph") / "data" / "predicate_mapping.csv"
-    with mapping_file.open(newline="") as handle:
+    with as_file(mapping_file) as path, path.open(newline="") as handle:
         classes = {row["r_id"]: (row["class"], row["subclass"]) for row in csv.DictReader(handle)}
 
     assert {r_id: classes.get(r_id) for r_id in DEFINING_ATTRIBUTES} == DEFINING_ATTRIBUTES
diff --git a/tests/test_edges_same_connection_regression.py b/tests/test_edges_same_connection_regression.py
index 5fea044..104bce9 100644
--- a/tests/test_edges_same_connection_regression.py
+++ b/tests/test_edges_same_connection_regression.py
@@ -33,7 +33,9 @@ def test_edges_are_classified_from_the_relationship_mapping(
     assert edge.predicate_subkind == "mapping"
 
 
-@pytest.mark.parametrize(("kinds", "expected"), [({PredicateKind.IDENTITY}, 1), ({PredicateKind.HIERARCHY}, 0)])
+@pytest.mark.parametrize(
+    ("kinds", "expected"), [({PredicateKind.IDENTITY}, 1), ({PredicateKind.HIERARCHY}, 0)]
+)
 def test_predicate_kinds_filter_edges(mock_cdm_kg: KnowledgeGraph, kinds, expected) -> None:
     edges = mock_cdm_kg.edges(
         concept_ids=900001,
diff --git a/tests/test_embedding_optional.py b/tests/test_embedding_optional.py
index b73f243..7d420aa 100644
--- a/tests/test_embedding_optional.py
+++ b/tests/test_embedding_optional.py
@@ -9,9 +9,9 @@
 
 import numpy as np
 import pytest
-
 from oa_configurator.resolver import ResolvedModel, ResolvedProvider
 from omop_alchemy.cdm.query import ConceptFilter
+from omop_emb import MetricType
 
 from omop_graph.extensions import emb as emb_ext
 from omop_graph.extensions.emb import MissingExtensionError
@@ -45,9 +45,7 @@ class BrokenKG:
         def emb(self):
             raise MissingExtensionError()
 
-    assert (
-        emb_ext.get_embedding_reader_interface(cast(KnowledgeGraph, BrokenKG())) is None
-    )
+    assert emb_ext.get_embedding_reader_interface(cast(KnowledgeGraph, BrokenKG())) is None
 
 
 def test_knowledge_graph_emb_raises_missing_extension_error_when_omop_emb_unavailable(
@@ -103,19 +101,15 @@ def test_fallback_flag_true_logs_attempt_when_concepts_missing(
         write=True,
         backend=Mock(),
         resolved_model=_make_resolved_model(),
-        metric_type=cast(emb_ext.EmbeddingMetricType, "cosine"),
+        metric_type=MetricType.COSINE,
     )
 
     fake_reader = Mock()
     # concept 196653 ("Malignant tumor of kidney") exists in the dummy CDM DB
-    fake_reader.get_concepts_without_embedding.return_value = {
-        196653: "Malignant tumor of kidney"
-    }
+    fake_reader.get_concepts_without_embedding.return_value = {196653: "Malignant tumor of kidney"}
 
     monkeypatch.setattr(emb_ext, "HAS_OMOP_EMB", True)
-    monkeypatch.setattr(
-        emb_ext, "get_embedding_reader_interface", lambda _: fake_reader
-    )
+    monkeypatch.setattr(emb_ext, "get_embedding_reader_interface", lambda _: fake_reader)
     # No writer injected: simulates a writer that failed to materialize for some
     # other reason (config says write=True, but the lookup itself returns None).
     monkeypatch.setattr(emb_ext, "try_get_embedding_writer_interface", lambda _: None)
@@ -124,9 +118,7 @@ def test_fallback_flag_true_logs_attempt_when_concepts_missing(
     with caplog.at_level(logging.DEBUG, logger="omop_graph.extensions.emb"):
         emb_ext.semantic_similarity(
             kg=mock_cdm_kg,
-            standard_concepts=[
-                _make_standard_concept(196653, "Malignant tumor of kidney")
-            ],
+            standard_concepts=[_make_standard_concept(196653, "Malignant tumor of kidney")],
             query_embedding=np.zeros((1, 3), dtype=np.float32),
         )
 
@@ -148,26 +140,20 @@ def test_fallback_flag_false_logs_disabled_when_concepts_missing(
         compute_missing_embeddings=False,
         backend=Mock(),
         resolved_model=_make_resolved_model(),
-        metric_type=cast(emb_ext.EmbeddingMetricType, "cosine"),
+        metric_type=MetricType.COSINE,
     )
 
     fake_reader = Mock()
-    fake_reader.get_concepts_without_embedding.return_value = {
-        196653: "Malignant tumor of kidney"
-    }
+    fake_reader.get_concepts_without_embedding.return_value = {196653: "Malignant tumor of kidney"}
 
     monkeypatch.setattr(emb_ext, "HAS_OMOP_EMB", True)
-    monkeypatch.setattr(
-        emb_ext, "get_embedding_reader_interface", lambda _: fake_reader
-    )
+    monkeypatch.setattr(emb_ext, "get_embedding_reader_interface", lambda _: fake_reader)
     monkeypatch.setattr(emb_ext, "get_neareast_concepts", lambda **_: None)
 
     with caplog.at_level(logging.INFO, logger="omop_graph.extensions.emb"):
         emb_ext.semantic_similarity(
             kg=mock_cdm_kg,
-            standard_concepts=[
-                _make_standard_concept(196653, "Malignant tumor of kidney")
-            ],
+            standard_concepts=[_make_standard_concept(196653, "Malignant tumor of kidney")],
             query_embedding=np.zeros((1, 3), dtype=np.float32),
         )
 
@@ -182,7 +168,7 @@ class TestEmbeddingConfigurationValidation:
     def test_compute_missing_embeddings_requires_write(self):
         with pytest.raises(ValueError, match="compute_missing_embeddings=True requires write=True"):
             KnowledgeGraphEmbeddingConfiguration(
-                metric_type=cast(emb_ext.EmbeddingMetricType, "cosine"),
+                metric_type=MetricType.COSINE,
                 backend=Mock(),
                 resolved_model=_make_resolved_model(),
                 compute_missing_embeddings=True,
@@ -191,7 +177,7 @@ def test_compute_missing_embeddings_requires_write(self):
     def test_model_name_and_provider_type_derive_from_resolved_model(self):
         resolved = _make_resolved_model()
         cfg = KnowledgeGraphEmbeddingConfiguration(
-            metric_type=cast(emb_ext.EmbeddingMetricType, "cosine"),
+            metric_type=MetricType.COSINE,
             backend=Mock(),
             resolved_model=resolved,
             write=True,
@@ -224,9 +210,7 @@ def emb(self):
         monkeypatch.setattr(emb_ext, "HAS_OMOP_EMB", True)
 
         with caplog.at_level(logging.DEBUG, logger="omop_graph.extensions.emb"):
-            result = emb_ext.try_get_embedding_writer_interface(
-                cast(KnowledgeGraph, ReadOnlyKG())
-            )
+            result = emb_ext.try_get_embedding_writer_interface(cast(KnowledgeGraph, ReadOnlyKG()))
         assert result is None
         assert "unittest.mock.Mock" in caplog.text
         assert "write=False" not in caplog.text
@@ -242,7 +226,9 @@ class NoConfigKG:
             def emb(self):
                 raise ValueError("Embedding configuration is not set.")
 
-        assert emb_ext.try_get_embedding_writer_interface(cast(KnowledgeGraph, NoConfigKG())) is None
+        assert (
+            emb_ext.try_get_embedding_writer_interface(cast(KnowledgeGraph, NoConfigKG())) is None
+        )
 
     def test_returns_none_when_extension_missing(self):
         class MissingExtensionKG:
@@ -250,7 +236,10 @@ class MissingExtensionKG:
             def emb(self):
                 raise MissingExtensionError()
 
-        assert emb_ext.try_get_embedding_writer_interface(cast(KnowledgeGraph, MissingExtensionKG())) is None
+        assert (
+            emb_ext.try_get_embedding_writer_interface(cast(KnowledgeGraph, MissingExtensionKG()))
+            is None
+        )
 
 
 # ── omop-emb#48 split: k threaded explicitly instead of via filter.limit ──
@@ -264,13 +253,14 @@ def test_semantic_similarity_splits_cdm_and_knn_filters(
     the KNN call gets a plain EmbeddingConceptFilter (no limit) with k passed
     explicitly instead.
     """
-    from omop_emb.utils.embedding_utils import CDMConceptFilter, EmbeddingConceptFilter
+    from omop_alchemy.cdm.query import ConceptFilter as CDMConceptFilter
+    from omop_emb.utils.embedding_utils import EmbeddingConceptFilter
 
     mock_cdm_kg._emb_config = KnowledgeGraphEmbeddingConfiguration(
         compute_missing_embeddings=False,
         backend=Mock(),
         resolved_model=_make_resolved_model(),
-        metric_type=cast(emb_ext.EmbeddingMetricType, "cosine"),
+        metric_type=MetricType.COSINE,
     )
 
     fake_reader = Mock()
@@ -280,9 +270,7 @@ def test_semantic_similarity_splits_cdm_and_knn_filters(
     fake_reader.get_nearest_concepts.return_value = ()
 
     monkeypatch.setattr(emb_ext, "HAS_OMOP_EMB", True)
-    monkeypatch.setattr(
-        emb_ext, "get_embedding_reader_interface", lambda _: fake_reader
-    )
+    monkeypatch.setattr(emb_ext, "get_embedding_reader_interface", lambda _: fake_reader)
 
     emb_ext.semantic_similarity(
         kg=mock_cdm_kg,
@@ -293,9 +281,7 @@ def test_semantic_similarity_splits_cdm_and_knn_filters(
         query_embedding=np.zeros((1, 3), dtype=np.float32),
     )
 
-    cdm_filter = fake_reader.get_concepts_without_embedding.call_args.kwargs[
-        "concept_filter"
-    ]
+    cdm_filter = fake_reader.get_concepts_without_embedding.call_args.kwargs["concept_filter"]
     assert isinstance(cdm_filter, CDMConceptFilter)
     assert cdm_filter.limit == 2
 
diff --git a/tests/test_fulltext_optional.py b/tests/test_fulltext_optional.py
index cbb4c43..d7cb23b 100644
--- a/tests/test_fulltext_optional.py
+++ b/tests/test_fulltext_optional.py
@@ -1,9 +1,10 @@
-import pytest
+from typing import Any
 
+import pytest
 import sqlalchemy as sa
+from omop_alchemy.backends import FullTextError
 from sqlalchemy import Engine
 from sqlalchemy.orm import sessionmaker
-from omop_alchemy.backends import FullTextError
 
 from omop_graph.graph.kg import KnowledgeGraph
 from omop_graph.graph.nodes import LabelMatchKind
@@ -13,9 +14,7 @@
 @pytest.mark.parametrize("synonym", [False, True])
 @pytest.mark.postgresql
 @pytest.mark.db_dialect
-def test_fulltext_query_requires_tsvector_columns(
-    synonym: bool, mock_cdm_engine: Engine
-):
+def test_fulltext_query_requires_tsvector_columns(synonym: bool, mock_cdm_engine: Engine):
     """Full-text query raises FullTextError when tsvector columns are absent from the database.
 
     The mock CDM's own table creation never adds tsvector columns (that's a
@@ -23,9 +22,7 @@ def test_fulltext_query_requires_tsvector_columns(
     q_concept_name_fulltext (which inspects the live DB schema) always fires here.
     """
     with pytest.raises(FullTextError):
-        q_concept_name_fulltext(
-            "kidney cancer", synonym=synonym, engine=mock_cdm_engine
-        )
+        q_concept_name_fulltext("kidney cancer", synonym=synonym, engine=mock_cdm_engine)
 
 
 def test_knowledge_graph_fulltext_lookup_skips_unsupported_sqlite(
@@ -36,7 +33,7 @@ def test_knowledge_graph_fulltext_lookup_skips_unsupported_sqlite(
 
 
 def test_knowledge_graph_rejects_engine_instead_of_session_factory():
-    engine = sa.create_engine("sqlite+pysqlite:///:memory:")
+    engine: Any = sa.create_engine("sqlite+pysqlite:///:memory:")
     try:
         with pytest.raises(TypeError, match="callable session factory.*not an Engine"):
             KnowledgeGraph(engine)
diff --git a/tests/test_fulltext_vocab_schema_postgres.py b/tests/test_fulltext_vocab_schema_postgres.py
index b9f0065..f33a52c 100644
--- a/tests/test_fulltext_vocab_schema_postgres.py
+++ b/tests/test_fulltext_vocab_schema_postgres.py
@@ -10,6 +10,7 @@
 
 import pytest
 import sqlalchemy as sa
+from fixtures.helpers import VOCAB_TABLES
 from oa_configurator import Role
 from oa_configurator.testing import scoped_test_schema
 from omop_alchemy.backends import CONCEPT_NAME_TSVECTOR_COLUMN, FullTextError, resolve_backend
@@ -17,8 +18,6 @@
 
 from omop_graph.graph.queries import q_concept_name_fulltext
 
-from fixtures.helpers import VOCAB_TABLES
-
 pytestmark = [pytest.mark.postgresql, pytest.mark.db_dialect]
 
 
@@ -42,7 +41,9 @@ def test_fulltext_query_finds_the_tsvector_column_in_the_vocab_schema(pg_db):
         # split, not a lucky same-schema coincidence.
         inspector = sa.inspect(engine)
         assert not inspector.has_table("concept", schema=scoped.schemas[Role.PRIMARY])
-        columns = {c["name"] for c in inspector.get_columns("concept", schema=scoped.schemas[Role.VOCAB])}
+        columns = {
+            c["name"] for c in inspector.get_columns("concept", schema=scoped.schemas[Role.VOCAB])
+        }
         assert CONCEPT_NAME_TSVECTOR_COLUMN in columns
 
         stmt = q_concept_name_fulltext("kidney cancer", engine=engine)
diff --git a/tests/test_grounding.py b/tests/test_grounding.py
index f91216d..ed711c8 100644
--- a/tests/test_grounding.py
+++ b/tests/test_grounding.py
@@ -1,9 +1,10 @@
 from __future__ import annotations
 
 import pytest
+from fixtures.mock_cdm import PARENT_CANCER_ID
+from omop_alchemy.cdm.query import ConceptFilter
 
 from omop_graph.extensions.omop_alchemy import PredicateKind
-from omop_alchemy.cdm.query import ConceptFilter
 from omop_graph.graph.kg import KnowledgeGraph
 from omop_graph.reasoning.grounding import (
     GroundingConstraints,
@@ -17,7 +18,6 @@
     PartialLabelResolver,
     PartialSynonymResolver,
 )
-from fixtures.mock_cdm import PARENT_CANCER_ID
 
 
 class TestQueryTextWithContext:
diff --git a/tests/test_kg_ancestry.py b/tests/test_kg_ancestry.py
index dfe4dba..9e2ee60 100644
--- a/tests/test_kg_ancestry.py
+++ b/tests/test_kg_ancestry.py
@@ -1,10 +1,12 @@
 from __future__ import annotations
 
+from fixtures.mock_cdm import CONCEPT_META_ID, PARENT_CANCER_ID
+
 from omop_graph.graph.kg import KnowledgeGraph
 from omop_graph.graph.nodes import LabelMatchKind
 from omop_graph.graph.paths import find_standard_paths
 from omop_graph.reasoning.resolvers.resolvers import CandidateHit
-from fixtures.mock_cdm import PARENT_CANCER_ID, CONCEPT_META_ID
+
 
 def test_get_potential_ancestors_batch_returns_only_real_ancestors(
     mock_cdm_kg: KnowledgeGraph,
diff --git a/tests/test_oaklib_interface.py b/tests/test_oaklib_interface.py
index ebfe371..13652d0 100644
--- a/tests/test_oaklib_interface.py
+++ b/tests/test_oaklib_interface.py
@@ -9,9 +9,7 @@ def test_annotate_text_standardizes_non_standard_candidate_without_parent_annota
 ) -> None:
     interface = OMOPTextAnnotatorInterface(kg=mock_cdm_kg)
 
-    annotations = list(
-        interface.annotate_text(text="Kidney carcinoma term", annotations=None)
-    )
+    annotations = list(interface.annotate_text(text="Kidney carcinoma term", annotations=None))
 
     assert len(annotations) == 1
     assert annotations[0].object_id == "OMOP:196653"
diff --git a/tests/test_oaklib_schema_awareness.py b/tests/test_oaklib_schema_awareness.py
index bca03d2..d667097 100644
--- a/tests/test_oaklib_schema_awareness.py
+++ b/tests/test_oaklib_schema_awareness.py
@@ -16,15 +16,15 @@
 import pytest
 import sqlalchemy as sa
 import sqlalchemy.orm
-from oaklib import get_adapter
-
+from fixtures.helpers import require_cdm
+from fixtures.mock_cdm import seed_relationship_vocabulary
 from oa_configurator import Resolver, Role
 from oa_configurator.testing import scoped_test_schema
+from oaklib import get_adapter
 from omop_alchemy.cdm.model.vocabulary import Concept, Concept_Class, Domain, Vocabulary
 from omop_alchemy.cross_database import cdm_sessionmaker
-from orm_loader.backends import staging_schema_claim
+from orm_loader.backends import resolve_backend, staging_schema_claim
 from orm_loader.helpers import Base
-from orm_loader.backends import resolve_backend
 
 from omop_graph.cli import relationship_classification
 from omop_graph.db.session import resolve_cdm_database
@@ -32,7 +32,6 @@
 from omop_graph.graph.kg import KnowledgeGraph
 from omop_graph.oaklib_interface import omop_implementation
 from omop_graph.oaklib_interface.omop_implementation import OMOPAlchemyImplementation
-from fixtures.mock_cdm import seed_relationship_vocabulary
 
 _META_CONCEPT_ID = 0
 _CONCEPT_ID = 1001
@@ -70,7 +69,11 @@ def _seed_one_concept(engine: sa.Engine, *, concept_id: int, name: str) -> None:
                         valid_start_date=_TODAY,
                         valid_end_date=_FAR_FUTURE,
                     ),
-                    Domain(domain_id="Metadata", domain_name="Metadata", domain_concept_id=_META_CONCEPT_ID),
+                    Domain(
+                        domain_id="Metadata",
+                        domain_name="Metadata",
+                        domain_concept_id=_META_CONCEPT_ID,
+                    ),
                     Vocabulary(
                         vocabulary_id="OMOP",
                         vocabulary_name="OMOP",
@@ -96,11 +99,15 @@ def _populate(scoped, *, name: str) -> None:
     relationship_classification(resolved=scoped.resolved)
 
 
-@pytest.mark.parametrize(("descriptor", "expected_name"), [("omop:named_db", "named_db"), ("omop:", None)])
+@pytest.mark.parametrize(
+    ("descriptor", "expected_name"), [("omop:named_db", "named_db"), ("omop:", None)]
+)
 def test_get_adapter_resolves_the_slug_as_the_database_name(
     pg_db, monkeypatch, descriptor, expected_name
 ) -> None:
-    with scoped_test_schema(pg_db.resolved, prefix="oaklib_get_adapter", schema_claims=[staging_schema_claim()]) as scoped:
+    with scoped_test_schema(
+        pg_db.resolved, prefix="oaklib_get_adapter", schema_claims=[staging_schema_claim()]
+    ) as scoped:
         _populate(scoped, name="Adapter concept")
 
         def fake_resolve_cdm_database(name):
@@ -126,14 +133,18 @@ def test_resolve_cdm_database_resolves_a_named_entry(pg_db) -> None:
 
 
 def test_resolved_path_reuses_one_engine_without_a_vocab_split(pg_db) -> None:
-    with scoped_test_schema(pg_db.resolved, prefix="oaklib_resolved", schema_claims=[staging_schema_claim()]) as scoped:
+    with scoped_test_schema(
+        pg_db.resolved, prefix="oaklib_resolved", schema_claims=[staging_schema_claim()]
+    ) as scoped:
         _populate(scoped, name="Resolved-path concept")
 
-        adapter = OMOPAlchemyImplementation(resolved=scoped.resolved)
+        adapter = OMOPAlchemyImplementation(resolved=require_cdm(scoped.resolved))
 
         assert _one_engine_for_both_roles(adapter.kg)
         with adapter.kg.session_factory() as session:
-            assert session.get_bind(Concept).pool._pre_ping is True
+            bind = session.get_bind(Concept)
+            assert isinstance(bind, sa.Engine)
+            assert bind.pool._pre_ping is True
         assert adapter.label(f"OMOP:{_CONCEPT_ID}") == "Resolved-path concept"
 
 
@@ -145,25 +156,41 @@ def test_two_connection_entries_for_one_database_collapse_to_one_engine(pg_db) -
     name = pg_db.resolved.name
     resolver = Resolver.from_active_config()
     resolver = resolver.with_overrides(
-        connections={"oaklib_split_vocab": resolver.config.connections[pg_db.resolved.connection.name]},
-        databases={name: resolver.config.databases[name].model_copy(update={"vocab_connection": "oaklib_split_vocab"})},
+        connections={
+            "oaklib_split_vocab": resolver.config.connections[pg_db.resolved.connection.name]
+        },
+        databases={
+            name: resolver.config.databases[name].model_copy(
+                update={"vocab_connection": "oaklib_split_vocab"}
+            )
+        },
     )
-    with scoped_test_schema(resolver.resolve_database(name), prefix="oaklib_split", resolver=resolver, schema_claims=[staging_schema_claim()]) as scoped:
-        assert scoped.resolved.connection.name != scoped.resolved.vocab_connection.name
-        assert scoped.resolved.foreign_key_can_span(Role.PRIMARY, Role.VOCAB)
+    with scoped_test_schema(
+        resolver.resolve_database(name),
+        prefix="oaklib_split",
+        resolver=resolver,
+        schema_claims=[staging_schema_claim()],
+    ) as scoped:
+        resolved = require_cdm(scoped.resolved)
+        assert resolved.connection.name != resolved.vocab_connection.name
+        assert resolved.foreign_key_can_span(Role.PRIMARY, Role.VOCAB)
         _populate(scoped, name="Split-wiring concept")
 
-        adapter = OMOPAlchemyImplementation(resolved=scoped.resolved)
+        adapter = OMOPAlchemyImplementation(resolved=resolved)
 
         assert _one_engine_for_both_roles(adapter.kg)
         assert adapter.label(f"OMOP:{_CONCEPT_ID}") == "Split-wiring concept"
 
 
 def test_kg_injection_path_uses_the_injected_knowledge_graph(pg_db) -> None:
-    with scoped_test_schema(pg_db.resolved, prefix="oaklib_kg", schema_claims=[staging_schema_claim()]) as scoped:
+    with scoped_test_schema(
+        pg_db.resolved, prefix="oaklib_kg", schema_claims=[staging_schema_claim()]
+    ) as scoped:
         _populate(scoped, name="Injected concept")
         kg = KnowledgeGraph(
-            cdm_sessionmaker(scoped.resolved, primary=scoped.engine, vocab=scoped.engine)
+            cdm_sessionmaker(
+                require_cdm(scoped.resolved), primary=scoped.engine, vocab=scoped.engine
+            )
         )
 
         adapter = OMOPAlchemyImplementation(kg=kg)
diff --git a/tests/test_predicate_mapping_data.py b/tests/test_predicate_mapping_data.py
index 490371e..fbfb94a 100644
--- a/tests/test_predicate_mapping_data.py
+++ b/tests/test_predicate_mapping_data.py
@@ -1,21 +1,18 @@
 import csv
+import io
 from importlib import resources
 
 
 def _read_csv(filename: str) -> list[dict[str, str]]:
     path = resources.files("omop_graph").joinpath("data", filename)
-    with path.open("r", encoding="utf-8-sig", newline="") as source:
+    with path.open("rb") as raw, io.TextIOWrapper(raw, encoding="utf-8-sig", newline="") as source:
         return list(csv.DictReader(source))
 
 
 def test_every_mapping_class_subclass_has_a_classification():
-    mapping_pairs = {
-        (row["class"], row["subclass"])
-        for row in _read_csv("predicate_mapping.csv")
-    }
+    mapping_pairs = {(row["class"], row["subclass"]) for row in _read_csv("predicate_mapping.csv")}
     classification_pairs = {
-        (row["class"], row["subclass"])
-        for row in _read_csv("predicate_classification.csv")
+        (row["class"], row["subclass"]) for row in _read_csv("predicate_classification.csv")
     }
 
     assert mapping_pairs <= classification_pairs
diff --git a/tests/test_relationship_classification.py b/tests/test_relationship_classification.py
index af1bb27..177f3ff 100644
--- a/tests/test_relationship_classification.py
+++ b/tests/test_relationship_classification.py
@@ -12,24 +12,26 @@
 import logging
 
 import sqlalchemy as sa
+from fixtures.helpers import require_cdm
+from fixtures.mock_cdm import seed_relationship_vocabulary
 from oa_configurator import Role
 from oa_configurator.testing import scoped_test_schema
-
 from orm_loader.backends import staging_schema_claim
 from orm_loader.helpers import Base
 
 from omop_graph.cli import relationship_classification
 from omop_graph.extensions.omop_alchemy import RelationshipClass, RelationshipMapping
-from fixtures.mock_cdm import seed_relationship_vocabulary
 
 
 def test_relationship_classification_respects_the_configured_schema(pg_db):
-    with scoped_test_schema(pg_db.resolved, prefix="relclass_routing", schema_claims=[staging_schema_claim()]) as scoped:
+    with scoped_test_schema(
+        pg_db.resolved, prefix="relclass_routing", schema_claims=[staging_schema_claim()]
+    ) as scoped:
         schema = scoped.schemas[Role.PRIMARY]
         Base.metadata.create_all(bind=scoped.engine, checkfirst=True)
         seed_relationship_vocabulary(scoped.engine)
 
-        relationship_classification(resolved=scoped.resolved)
+        relationship_classification(resolved=require_cdm(scoped.resolved))
 
         with scoped.engine.connect() as conn:
             n_class = conn.execute(
@@ -54,12 +56,14 @@ def test_relationship_classification_respects_the_configured_schema(pg_db):
 def test_relationship_classification_is_idempotent(pg_db):
     """Re-running against the same schema, the real-world redeploy case the
     DROP TABLE/enum-drop cleanup exists for, must not fail."""
-    with scoped_test_schema(pg_db.resolved, prefix="relclass_idempotent", schema_claims=[staging_schema_claim()]) as scoped:
+    with scoped_test_schema(
+        pg_db.resolved, prefix="relclass_idempotent", schema_claims=[staging_schema_claim()]
+    ) as scoped:
         Base.metadata.create_all(bind=scoped.engine, checkfirst=True)
         seed_relationship_vocabulary(scoped.engine)
 
-        relationship_classification(resolved=scoped.resolved)
-        relationship_classification(resolved=scoped.resolved)
+        relationship_classification(resolved=require_cdm(scoped.resolved))
+        relationship_classification(resolved=require_cdm(scoped.resolved))
 
         with scoped.engine.connect() as conn:
             n_class = conn.execute(
@@ -68,9 +72,7 @@ def test_relationship_classification_is_idempotent(pg_db):
         assert n_class and n_class > 0
 
 
-def test_relationship_classification_warns_and_drops_unknown_ids_colocated(
-    pg_db, tmp_path, caplog
-):
+def test_relationship_classification_warns_and_drops_unknown_ids_colocated(pg_db, tmp_path, caplog):
     (tmp_path / "predicate_classification.csv").write_text(
         "class,subclass,description,semantics,inference\n"
         "Identity,mapping,Identity mapping,identity,none\n"
@@ -90,13 +92,11 @@ def test_relationship_classification_warns_and_drops_unknown_ids_colocated(
         seed_relationship_vocabulary(scoped.engine)
 
         with caplog.at_level(logging.WARNING, logger="omop_graph.cli"):
-            relationship_classification(str(tmp_path), resolved=scoped.resolved)
+            relationship_classification(str(tmp_path), resolved=require_cdm(scoped.resolved))
 
         with scoped.engine.connect() as connection:
             relationship_ids = set(
-                connection.execute(
-                    sa.select(RelationshipMapping.relationship_id)
-                ).scalars()
+                connection.execute(sa.select(RelationshipMapping.relationship_id)).scalars()
             )
 
     assert relationship_ids == {"Maps to"}
diff --git a/tests/test_relationship_classification_duckdb.py b/tests/test_relationship_classification_duckdb.py
index 73cc635..3997c31 100644
--- a/tests/test_relationship_classification_duckdb.py
+++ b/tests/test_relationship_classification_duckdb.py
@@ -32,9 +32,7 @@ def _seed_duckdb_vocabulary(engine, mapping_rows: list[dict[str, str]]) -> set[s
         sa.Column("relationship_id", sa.String(20), primary_key=True),
         schema=Role.VOCAB.value,
     )
-    relationship_ids = {
-        row["r_id"].strip() for row in mapping_rows if row.get("r_id", "").strip()
-    }
+    relationship_ids = {row["r_id"].strip() for row in mapping_rows if row.get("r_id", "").strip()}
     with engine.begin() as connection:
         relationship_table.create(connection)
         connection.execute(
@@ -70,15 +68,18 @@ def test_relationship_classification_runs_and_is_idempotent_on_duckdb(
     cli.relationship_classification(resolved=resolved)
 
     with duckdb_db.committing_engine.connect() as connection:
-        checks = connection.execute(
-            sa.text(
-                "SELECT constraint_text FROM duckdb_constraints() "
-                "WHERE table_name = 'relationship_class' AND constraint_type = 'CHECK'"
+        checks = (
+            connection.execute(
+                sa.text(
+                    "SELECT constraint_text FROM duckdb_constraints() "
+                    "WHERE table_name = 'relationship_class' AND constraint_type = 'CHECK'"
+                )
             )
-        ).scalars().all()
+            .scalars()
+            .all()
+        )
         assert any(
-            "predicate_kind" in check
-            and all(f"'{kind.value}'" in check for kind in PredicateKind)
+            "predicate_kind" in check and all(f"'{kind.value}'" in check for kind in PredicateKind)
             for check in checks
         )
         actual_classes = {
diff --git a/tests/test_resolvers_mock.py b/tests/test_resolvers_mock.py
index e4ff0a4..ad38503 100644
--- a/tests/test_resolvers_mock.py
+++ b/tests/test_resolvers_mock.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Dict, List, cast
+from typing import cast
 
 from omop_graph.graph.kg import KnowledgeGraph
 from omop_graph.graph.nodes import LabelMatch, LabelMatchKind
@@ -17,7 +17,7 @@
 @dataclass
 class _Case:
     text: str
-    hits: Dict[str, List[int]]
+    hits: dict[str, list[int]]
 
 
 class _KG:
diff --git a/tests/test_schema_provenance_guard.py b/tests/test_schema_provenance_guard.py
index 98acaa3..e0855c4 100644
--- a/tests/test_schema_provenance_guard.py
+++ b/tests/test_schema_provenance_guard.py
@@ -16,6 +16,8 @@
 from __future__ import annotations
 
 import pytest
+from fixtures.helpers import require_cdm
+from fixtures.mock_cdm import seed_relationship_vocabulary
 from oa_configurator import Role, SchemaDriftError
 from oa_configurator.testing import (
     guarded_resolver,
@@ -23,11 +25,9 @@
     resolve_with_role_schemas,
     scoped_test_schema,
 )
-
 from orm_loader.helpers import Base
 
 from omop_graph import cli as omop_graph_cli
-from fixtures.mock_cdm import seed_relationship_vocabulary
 
 pytestmark = [pytest.mark.postgresql, pytest.mark.db_dialect]
 
@@ -51,4 +51,4 @@ def test_relationship_classification_guard_fires_on_reconfigured_schema(
         resolver=resolver,
     )
     with pytest.raises(SchemaDriftError):
-        omop_graph_cli.relationship_classification(resolved=drifted)
+        omop_graph_cli.relationship_classification(resolved=require_cdm(drifted))
diff --git a/tests/test_vocab_split_connection.py b/tests/test_vocab_split_connection.py
index e121da2..6482dc4 100644
--- a/tests/test_vocab_split_connection.py
+++ b/tests/test_vocab_split_connection.py
@@ -12,19 +12,20 @@
 
 from __future__ import annotations
 
+from collections.abc import Iterator
 from datetime import date
-from typing import Iterator, NamedTuple
+from typing import NamedTuple
 
 import pytest
 import sqlalchemy as sa
 import sqlalchemy.orm as so
-
+from fixtures.helpers import VOCAB_TABLES, require_cdm, table_of
 from oa_configurator import ResolvedCDMDatabase, Resolver, Role
-from oa_configurator.testing import isolated_test_database, resolve_with_role_schemas, scoped_test_schema
-from orm_loader.config import OrmLoaderConfig
-from orm_loader.helpers import Base
-from orm_loader.backends import resolve_backend
-
+from oa_configurator.testing import (
+    isolated_test_database,
+    resolve_with_role_schemas,
+    scoped_test_schema,
+)
 from omop_alchemy.cdm.model.vocabulary import (
     Concept,
     Concept_Class,
@@ -34,6 +35,9 @@
     Vocabulary,
 )
 from omop_alchemy.cross_database import cdm_sessionmaker
+from orm_loader.backends import resolve_backend
+from orm_loader.config import OrmLoaderConfig
+from orm_loader.helpers import Base
 
 from omop_graph.cli import relationship_classification
 from omop_graph.config import OmopGraphConfig
@@ -44,8 +48,6 @@
 )
 from omop_graph.graph.kg import KnowledgeGraph
 
-from fixtures.helpers import VOCAB_TABLES
-
 pytestmark = [pytest.mark.postgresql, pytest.mark.db_dialect]
 
 META_CONCEPT_ID = 0
@@ -56,7 +58,7 @@
 
 # Relationship/Concept_Relationship aren't in the shared minimal vocab
 # bootstrap, but are exactly what the split-connection merge is testing.
-_VOCAB_TABLES = VOCAB_TABLES + (Relationship.__table__, Concept_Relationship.__table__)
+_VOCAB_TABLES = VOCAB_TABLES + (table_of(Relationship), table_of(Concept_Relationship))
 
 # Postgres has no cross-database inline FK, so RelationshipMapping's FK to
 # relationship.relationship_id can't be created as DDL across genuinely
@@ -115,31 +117,39 @@ def split_engines() -> Iterator[_Engines]:
         isolated_test_database(OmopGraphConfig, "test_cdm_db_pg") as primary_db,
         isolated_test_database(OrmLoaderConfig, "test_orm_db_pg") as vocab_db,
     ):
-        name = primary_db.resolved.name
+        vocab_resolved = require_cdm(vocab_db.resolved)
+        name = require_cdm(primary_db.resolved).name
         resolver = Resolver.from_active_config()
         resolver = resolver.with_overrides(
             databases={
                 name: resolver.config.databases[name].model_copy(
-                    update={"vocab_connection": vocab_db.resolved.connection.name}
+                    update={"vocab_connection": vocab_resolved.connection.name}
                 )
             }
         )
-        split = resolver.resolve_database(name)
+        split = require_cdm(resolver.resolve_database(name))
         with scoped_test_schema(split, prefix="vocab_split", resolver=resolver) as scoped:
-            primary_engine, vocab_engine = scoped.resolved.create_engines()
+            scoped_resolved = require_cdm(scoped.resolved)
+            primary_engine, vocab_engine = scoped_resolved.create_engines()
             # Vocab tables are created through the vocab server's own resolved:
             # create_all on the split vocab engine also emits the PRIMARY-tagged
             # predicatekind type, whose schema only exists on the primary server.
             vocab_schema = scoped.schemas[Role.VOCAB]
-            vocab_only = resolve_with_role_schemas(
-                vocab_db.resolved, {role: vocab_schema for role in vocab_db.resolved.schema_tags()}
+            vocab_only = require_cdm(
+                resolve_with_role_schemas(
+                    vocab_resolved, {role: vocab_schema for role in vocab_resolved.schema_tags()}
+                )
             )
             vocab_setup_engine, _ = vocab_only.create_engines()
             try:
                 _shadow_metadata.create_all(primary_engine)
                 Base.metadata.create_all(vocab_setup_engine, tables=_VOCAB_TABLES, checkfirst=True)
                 _seed(primary_engine, vocab_setup_engine)
-                yield _Engines(primary=primary_engine, vocab=vocab_engine, resolved=scoped.resolved)
+                yield _Engines(
+                    primary=primary_engine,
+                    vocab=vocab_engine,
+                    resolved=scoped_resolved,
+                )
             finally:
                 vocab_setup_engine.dispose()
                 primary_engine.dispose()
@@ -184,8 +194,16 @@ def _seed(primary_engine: sa.Engine, vocab_engine: sa.Engine) -> None:
                         valid_start_date=_TODAY,
                         valid_end_date=_FAR_FUTURE,
                     ),
-                    Domain(domain_id="Metadata", domain_name="Metadata", domain_concept_id=META_CONCEPT_ID),
-                    Domain(domain_id="Condition", domain_name="Condition", domain_concept_id=META_CONCEPT_ID),
+                    Domain(
+                        domain_id="Metadata",
+                        domain_name="Metadata",
+                        domain_concept_id=META_CONCEPT_ID,
+                    ),
+                    Domain(
+                        domain_id="Condition",
+                        domain_name="Condition",
+                        domain_concept_id=META_CONCEPT_ID,
+                    ),
                     Vocabulary(
                         vocabulary_id="OMOP",
                         vocabulary_name="OMOP",
@@ -334,7 +352,9 @@ def test_concept_id_by_code_resolves_against_the_vocab_connection(split_engines:
     assert kg.concept_id_by_code("SNOMED", "SUBJ") == SUBJECT_CONCEPT_ID
 
 
-def test_concept_ids_by_label_resolves_against_the_vocab_connection(split_engines: _Engines) -> None:
+def test_concept_ids_by_label_resolves_against_the_vocab_connection(
+    split_engines: _Engines,
+) -> None:
     kg = _split_kg(split_engines)
 
     assert kg.concept_ids_by_label("Subject concept") == (SUBJECT_CONCEPT_ID,)
diff --git a/uv.lock b/uv.lock
index d5b3df3..cf3bb52 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 5
 requires-python = ">=3.12"
 resolution-markers = [
     "python_full_version >= '3.15' and sys_platform == 'win32'",
@@ -2301,8 +2301,8 @@ requires-dist = [
     { name = "pytest-cov", marker = "extra == 'dev'", specifier = ">=7.0.0" },
     { name = "rich", specifier = ">=13.0" },
     { name = "rich", marker = "extra == 'dev'", specifier = ">=14.2.0" },
-    { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.14.10" },
-    { name = "ty", marker = "extra == 'dev'", specifier = ">=0.0.59" },
+    { name = "ruff", marker = "extra == 'dev'", specifier = "==0.17.1" },
+    { name = "ty", marker = "extra == 'dev'", specifier = "==0.0.90" },
     { name = "typer", specifier = ">=0.12" },
     { name = "typing-extensions", specifier = ">=4.15.0" },
 ]
@@ -3337,27 +3337,27 @@ wheels = [
 
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-source = { registry = "https://pypi.org/simple" }
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