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--- |
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license: mit |
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task_categories: |
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- text-classification |
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- feature-extraction |
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language: |
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- en |
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tags: |
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- entity-resolution |
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- named-entity-recognition |
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- company-data |
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- embeddings |
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- sqlite |
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- vector-search |
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size_categories: |
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- 1M<n<10M |
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--- |
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# Entity References Database |
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A pre-built SQLite database with vector embeddings for organization/entity resolution. Used by the [corp-extractor](https://pypi.org/project/corp-extractor/) library for fast entity qualification via embedding similarity search. |
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## Overview |
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This database contains organization records from multiple authoritative sources, each with: |
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- Organization name (canonical) |
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- Source identifier (LEI, CIK, UK Company Number, Wikidata QID) |
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- Entity type classification (business, nonprofit, government, etc.) |
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- Vector embeddings for semantic search (768-dim, using [embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m)) |
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## Data Sources |
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| Source | Records | Identifier | Description | |
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|--------|---------|------------|-------------| |
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| GLEIF | ~3.2M | LEI (Legal Entity Identifier) | Global legal entities from the LEI system | |
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| SEC Edgar | ~100K+ | CIK (Central Index Key) | US SEC-registered filers | |
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| Companies House | ~5M | UK Company Number | UK registered companies | |
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| Wikidata | Variable | Wikidata QID | Notable organizations from Wikidata | |
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## Entity Types |
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Organizations are classified into the following types: |
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| Category | Types | |
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|----------|-------| |
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| Business | `business`, `fund`, `branch` | |
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| Non-profit | `nonprofit`, `ngo`, `foundation`, `trade_union` | |
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| Government | `government`, `international_org`, `political_party` | |
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| Other | `educational`, `research`, `healthcare`, `media`, `sports`, `religious`, `unknown` | |
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## Database Variants |
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| File | Description | Use Case | |
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|------|-------------|----------| |
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| `entities.db` | Full database with complete source record metadata | When you need full record details | |
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| `entities-lite.db` | Lite version without record data | Default - faster download, smaller size | |
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| `entities.db.gz` | Compressed full database | When bandwidth is limited | |
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| `entities-lite.db.gz` | Compressed lite database | Smallest download size | |
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## Schema |
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### organizations table |
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```sql |
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CREATE TABLE organizations ( |
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id INTEGER PRIMARY KEY AUTOINCREMENT, |
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name TEXT NOT NULL, |
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name_normalized TEXT NOT NULL, |
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source TEXT NOT NULL, -- 'gleif', 'sec_edgar', 'companies_house', 'wikipedia' |
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source_id TEXT NOT NULL, |
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region TEXT NOT NULL DEFAULT '', |
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entity_type TEXT NOT NULL DEFAULT 'unknown', |
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record TEXT NOT NULL, -- JSON with full source record (empty in lite version) |
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UNIQUE(source, source_id) |
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); |
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``` |
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### organization_embeddings table (sqlite-vec) |
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```sql |
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CREATE VIRTUAL TABLE organization_embeddings USING vec0( |
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org_id INTEGER PRIMARY KEY, |
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embedding float[768] |
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); |
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``` |
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## Usage with corp-extractor |
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```bash |
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# Install |
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pip install corp-extractor |
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# Download the database (lite version by default) |
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corp-extractor db download |
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# Download full version |
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corp-extractor db download --full |
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# Search for an organization |
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corp-extractor db search "Microsoft" |
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# Check database status |
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corp-extractor db status |
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``` |
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### Python API |
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```python |
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from statement_extractor.database import OrganizationDatabase, CompanyEmbedder |
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# Load database |
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database = OrganizationDatabase() |
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embedder = CompanyEmbedder() |
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# Search by embedding similarity |
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query_embedding = embedder.embed("Microsoft Corporation") |
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results = database.search(query_embedding, top_k=5) |
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for record, similarity in results: |
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print(f"{record.name} ({record.source}:{record.source_id}) - {similarity:.3f}") |
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``` |
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## Building Your Own Database |
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```bash |
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# Import from authoritative sources |
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corp-extractor db import-gleif --download |
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corp-extractor db import-sec --download |
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corp-extractor db import-companies-house --download |
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corp-extractor db import-wikidata --limit 50000 |
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# Upload to HuggingFace |
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export HF_TOKEN="hf_..." |
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corp-extractor db upload |
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``` |
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## License |
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MIT License - the database structure and embedding generation code are MIT licensed. |
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Individual data sources have their own licenses: |
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- GLEIF: Open license for LEI data |
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- SEC Edgar: Public domain (US government) |
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- Companies House: Open Government Licence |
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- Wikidata: CC0 (public domain) |
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## Links |
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- [Corp-Extractor on PyPI](https://pypi.org/project/corp-extractor/) |
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- [Corp-Extractor GitHub](https://github.com/corp-o-rate/statement-extractor) |
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- [Statement Extractor Model](https://huggingface.co/Corp-o-Rate-Community/statement-extractor) |
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