corp-extractor 0.9.3

Extract structured entity and relationship information from text

Corp Extractor

Extract structured subject-predicate-object statements from unstructured text using the T5-Gemma 2 model.

Features

• Person Database (v0.9.2): Qualify notable people (executives, politicians, athletes, etc.) against Wikidata with canonical IDs
• Organization Canonicalization (v0.9.2): Link equivalent records across sources (LEI, ticker, CIK, name matching)
• 5-Stage Pipeline (v0.8.0): Modular plugin-based architecture for full entity resolution
• Document Processing (v0.7.0): Process documents, URLs, and PDFs with chunking and deduplication
• Entity Embedding Database (v0.6.0): Fast entity qualification using vector similarity (~100K+ SEC, ~3M GLEIF, ~5M UK organizations)
• Structured Extraction: Converts unstructured text into subject-predicate-object triples
• Entity Type Recognition: Identifies 12 entity types (ORG, PERSON, GPE, LOC, PRODUCT, EVENT, etc.)
• Entity Qualification (v0.8.0): Adds identifiers (LEI, ticker, company numbers), canonical names, and FQN via embedding database
• Statement Labeling (v0.5.0): Sentiment analysis, relation type classification, confidence scoring
• GLiNER2 Integration (v0.4.0): Uses GLiNER2 (205M params) for entity recognition and relation extraction
• Predefined Predicates: Optional --predicates list for GLiNER2 relation extraction mode
• Beam Merging: Combines top beams for better coverage instead of picking one
• Embedding-based Dedup: Uses semantic similarity to detect near-duplicate predicates
• Predicate Taxonomies: Normalize predicates to canonical forms via embeddings
• Command Line Interface: Full-featured CLI with split, pipeline, document, and db commands
• Multiple Output Formats: Get results as Pydantic models, JSON, XML, or dictionaries

Installation

pip install corp-extractor

The GLiNER2 model (205M params) is downloaded automatically on first use.

Note: This package requires transformers>=5.0.0 for T5-Gemma2 model support.

For GPU support, install PyTorch with CUDA first:

pip install torch --index-url https://download.pytorch.org/whl/cu121
pip install corp-extractor

For Apple Silicon (M1/M2/M3), MPS acceleration is automatically detected:

pip install corp-extractor  # MPS used automatically

Quick Start

from statement_extractor import extract_statements

result = extract_statements("""
    Apple Inc. announced the iPhone 15 at their September event.
    Tim Cook presented the new features to customers worldwide.
""")

for stmt in result:
    print(f"{stmt.subject.text} ({stmt.subject.type})")
    print(f"  --[{stmt.predicate}]--> {stmt.object.text}")
    print(f"  Confidence: {stmt.confidence_score:.2f}")  # NEW in v0.2.0

Command Line Interface

The library includes a CLI for quick extraction from the terminal.

Install Globally (Recommended)

For best results, install globally first:

# Using uv (recommended)
uv tool install "corp-extractor[embeddings]"

# Using pipx
pipx install "corp-extractor[embeddings]"

# Using pip
pip install "corp-extractor[embeddings]"

# Then use anywhere
corp-extractor "Your text here"

Quick Run with uvx

Run directly without installing using uv:

uvx corp-extractor "Apple announced a new iPhone."

Note: First run downloads the model (~1.5GB) which may take a few minutes.

Usage Examples

The CLI provides three main commands: split, pipeline, and plugins.

# Simple extraction (Stage 1 only, fast)
corp-extractor split "Apple Inc. announced the iPhone 15."
corp-extractor split -f article.txt --json

# Full 5-stage pipeline (entity resolution, labeling, taxonomy)
corp-extractor pipeline "Amazon CEO Andy Jassy announced plans to hire workers."
corp-extractor pipeline -f article.txt --stages 1-3
corp-extractor pipeline "..." --disable-plugins sec_edgar

# Plugin management
corp-extractor plugins list
corp-extractor plugins list --stage 3
corp-extractor plugins info gleif_qualifier

Split Command (Simple Extraction)

corp-extractor split "Tim Cook is CEO of Apple." --json
corp-extractor split -f article.txt --beams 8 --verbose
cat article.txt | corp-extractor split -

Pipeline Command (Full Entity Resolution)

# Run all 5 stages
corp-extractor pipeline "Apple CEO Tim Cook announced..."

# Run specific stages
corp-extractor pipeline "..." --stages 1-3         # Stages 1, 2, 3
corp-extractor pipeline "..." --stages 1,2,5       # Stages 1, 2, 5
corp-extractor pipeline "..." --skip-stages 4,5    # Skip stages 4 and 5

# Plugin selection
corp-extractor pipeline "..." --plugins gleif,companies_house
corp-extractor pipeline "..." --disable-plugins sec_edgar

CLI Reference

Usage: corp-extractor [COMMAND] [OPTIONS]

Commands:
  split      Simple extraction (T5-Gemma only)
  pipeline   Full 5-stage pipeline with entity resolution
  plugins    List or inspect available plugins

Split Options:
  -f, --file PATH              Read input from file
  -o, --output [table|json|xml] Output format (default: table)
  --json / --xml               Output format shortcuts
  -b, --beams INTEGER          Number of beams (default: 4)
  --no-gliner                  Disable GLiNER2 extraction
  --predicates TEXT            Comma-separated predicates for relation extraction
  --device [auto|cuda|mps|cpu] Device to use (default: auto)
  -v, --verbose                Show confidence scores and metadata

Pipeline Options:
  --stages TEXT                Stages to run (e.g., '1-3' or '1,2,5')
  --skip-stages TEXT           Stages to skip (e.g., '4,5')
  --plugins TEXT               Enable only these plugins (comma-separated)
  --disable-plugins TEXT       Disable these plugins (comma-separated)
  -o, --output [table|json|yaml|triples]  Output format

Quality Scoring & Beam Merging

By default, the library:

• Scores each triple using semantic similarity (50%) + GLiNER2 entity recognition (50%)
• Merges top beams instead of selecting one, improving coverage
• Uses embeddings to detect semantically similar predicates ("bought" ≈ "acquired")

from statement_extractor import ExtractionOptions, ScoringConfig

# Precision mode - filter low-confidence triples
scoring = ScoringConfig(min_confidence=0.7)
options = ExtractionOptions(scoring_config=scoring)
result = extract_statements(text, options)

# Access confidence scores
for stmt in result:
    print(f"{stmt} (confidence: {stmt.confidence_score:.2f})")

New in v0.2.0: Predicate Taxonomies

Normalize predicates to canonical forms using embedding similarity:

from statement_extractor import PredicateTaxonomy, ExtractionOptions

taxonomy = PredicateTaxonomy(predicates=[
    "acquired", "founded", "works_for", "announced",
    "invested_in", "partnered_with"
])

options = ExtractionOptions(predicate_taxonomy=taxonomy)
result = extract_statements(text, options)

# "bought" -> "acquired" via embedding similarity
for stmt in result:
    if stmt.canonical_predicate:
        print(f"{stmt.predicate} -> {stmt.canonical_predicate}")

New in v0.2.2: Contextualized Matching

Predicate canonicalization and deduplication now use contextualized matching:

• Compares full "Subject Predicate Object" strings against source text
• Better accuracy because predicates are evaluated in context
• When duplicates are found, keeps the statement with the best match to source text

This means "Apple bought Beats" vs "Apple acquired Beats" are compared holistically, not just "bought" vs "acquired".

New in v0.2.3: Entity Type Merging & Reversal Detection

Entity Type Merging

When deduplicating statements, entity types are now automatically merged. If one statement has UNKNOWN type and a duplicate has a specific type (like ORG or PERSON), the specific type is preserved:

# Before deduplication:
# Statement 1: AtlasBio Labs (UNKNOWN) --sued by--> CuraPharm (ORG)
# Statement 2: AtlasBio Labs (ORG) --sued by--> CuraPharm (ORG)

# After deduplication:
# Single statement: AtlasBio Labs (ORG) --sued by--> CuraPharm (ORG)

Subject-Object Reversal Detection

The library now detects when subject and object may have been extracted in the wrong order by comparing embeddings against source text:

from statement_extractor import PredicateComparer

comparer = PredicateComparer()

# Automatically detect and fix reversals
fixed_statements = comparer.detect_and_fix_reversals(statements)

for stmt in fixed_statements:
    if stmt.was_reversed:
        print(f"Fixed reversal: {stmt}")

How it works:

1. For each statement with source text, compares:
   • "Subject Predicate Object" embedding vs source text
   • "Object Predicate Subject" embedding vs source text
2. If the reversed form has higher similarity, swaps subject and object
3. Sets was_reversed=True to indicate the correction

During deduplication, reversed duplicates (e.g., "A -> P -> B" and "B -> P -> A") are now detected and merged, with the correct orientation determined by source text similarity.

Pipeline Architecture

The library uses a 5-stage plugin-based pipeline for comprehensive entity resolution, statement enrichment, and taxonomy classification.

Pipeline Stages

Stage	Name	Input	Output	Key Tech
1	Splitting	Text	RawTriple[]	T5-Gemma2
2	Extraction	RawTriple[]	PipelineStatement[]	GLiNER2
3	Qualification	Entities	CanonicalEntity[]	Embedding DB
4	Labeling	Statements	LabeledStatement[]	Sentiment, etc.
5	Taxonomy	Statements	TaxonomyResult[]	MNLI, Embeddings

Pipeline Python API

from statement_extractor.pipeline import ExtractionPipeline, PipelineConfig

# Run full pipeline
pipeline = ExtractionPipeline()
ctx = pipeline.process("Amazon CEO Andy Jassy announced plans to hire workers.")

# Access results at each stage
print(f"Raw triples: {len(ctx.raw_triples)}")
print(f"Statements: {len(ctx.statements)}")
print(f"Labeled: {len(ctx.labeled_statements)}")

# Output with fully qualified names
for stmt in ctx.labeled_statements:
    print(f"{stmt.subject_fqn} --[{stmt.statement.predicate}]--> {stmt.object_fqn}")
    # e.g., "Andy Jassy (CEO, Amazon) --[announced]--> plans to hire workers"

Pipeline Configuration

from statement_extractor.pipeline import PipelineConfig, ExtractionPipeline

# Run only specific stages
config = PipelineConfig(
    enabled_stages={1, 2, 3},  # Skip labeling and taxonomy
    disabled_plugins={"person_qualifier"},  # Disable specific plugins
)
pipeline = ExtractionPipeline(config)
ctx = pipeline.process(text)

# Alternative: create config from stage string
config = PipelineConfig.from_stage_string("1-3")  # Stages 1, 2, 3

Built-in Plugins

Splitters (Stage 1):

• t5_gemma_splitter - T5-Gemma2 statement extraction

Extractors (Stage 2):

• gliner2_extractor - GLiNER2 entity recognition and relation extraction

Qualifiers (Stage 3):

• person_qualifier - PERSON → role, org, canonical ID via Wikidata person database (enhanced in v0.9.0)
• embedding_company_qualifier - ORG → canonical name, identifiers (LEI, CIK, company number), and FQN via embedding database

Labelers (Stage 4):

• sentiment_labeler - Statement sentiment analysis
• confidence_labeler - Confidence scoring
• relation_type_labeler - Relation type classification

Taxonomy Classifiers (Stage 5):

• mnli_taxonomy_classifier - MNLI zero-shot classification against ESG taxonomy
• embedding_taxonomy_classifier - Embedding similarity-based taxonomy classification

Taxonomy classifiers return multiple labels per statement above the confidence threshold.

Entity Database

The library includes an entity embedding database for fast entity qualification using vector similarity search. It stores records from authoritative sources (GLEIF, SEC, Companies House, Wikidata) with 768-dimensional embeddings for semantic matching.

Quick start:

corp-extractor db download              # Download pre-built database
corp-extractor db search "Microsoft"    # Search organizations
corp-extractor db search-people "Tim Cook"  # Search people

For comprehensive documentation including schema, CLI reference, Python API, and build instructions, see ENTITY_DATABASE.md.

New in v0.6.0: Entity Embedding Database

v0.6.0 introduces an entity embedding database for fast entity qualification using vector similarity search.

Data Sources

Organizations:

Source	Records	Identifier	EntityType Mapping
GLEIF	~3.2M	LEI (Legal Entity Identifier)	GENERAL→business, FUND→fund, BRANCH→branch, INTERNATIONAL_ORGANIZATION→international_org
SEC Edgar	~100K+	CIK (Central Index Key)	business (or fund via SIC codes)
Companies House	~5M	UK Company Number	Maps company_type to business/nonprofit
Wikidata	Variable	Wikidata QID	35+ query types mapped to EntityType

People (v0.9.0):

Source	Records	Identifier	PersonType Classification
Wikidata (SPARQL)	Variable	Wikidata QID	executive, politician, athlete, artist, academic, scientist, journalist, entrepreneur, activist
Wikidata (Dump)	All humans with enwiki	Wikidata QID	Classified from positions (P39) and occupations (P106)

Date Fields: All importers now include from_date and to_date where available:

• GLEIF: LEI registration date
• SEC Edgar: First SEC filing date
• Companies House: Incorporation and dissolution dates
• Wikidata Orgs: Inception (P571) and dissolution (P576) dates
• Wikidata People: Position start (P580) and end (P582) dates

Note: The same person can have multiple records with different role/org combinations (unique on source_id + role + org). Organizations discovered during people import are automatically inserted into the organizations table with known_for_org_id foreign key linking people to their organizations.

EntityType Classification

Each organization record is classified with an entity_type field:

Category	Types
Business	business, fund, branch
Non-profit	nonprofit, ngo, foundation, trade_union
Government	government, international_org, political_party
Other	educational, research, healthcare, media, sports, religious, unknown

Building the Database

# Import organizations from authoritative sources
corp-extractor db import-gleif --download
corp-extractor db import-sec --download      # Bulk submissions.zip (~100K+ filers)
corp-extractor db import-companies-house --download
corp-extractor db import-wikidata --limit 50000

# Import notable people (v0.9.0)
corp-extractor db import-people --type executive --limit 5000
corp-extractor db import-people --all --limit 10000  # All person types
corp-extractor db import-people --type executive --skip-existing  # Skip existing records
corp-extractor db import-people --type executive --enrich-dates   # Fetch role start/end dates

# Import from Wikidata dump (v0.9.1) - avoids SPARQL timeouts
corp-extractor db import-wikidata-dump --download --limit 50000   # Downloads ~100GB dump
corp-extractor db import-wikidata-dump --dump /path/to/dump.bz2 --people --no-orgs  # Local dump

# Check status
corp-extractor db status

# Search for an organization
corp-extractor db search "Microsoft"

# Search for a person (v0.9.0)
corp-extractor db search-people "Tim Cook"

Using in Pipeline

The database is automatically used by the embedding_company_qualifier plugin for Stage 3 (Qualification):

from statement_extractor.pipeline import ExtractionPipeline

pipeline = ExtractionPipeline()
ctx = pipeline.process("Microsoft acquired Activision Blizzard.")

for stmt in ctx.labeled_statements:
    print(f"{stmt.subject_fqn}")  # e.g., "Microsoft (sec_edgar:0000789019)"

Publishing to HuggingFace

# Upload database with all variants (full, lite, compressed)
export HF_TOKEN="hf_..."
corp-extractor db upload                     # Uses default cache location
corp-extractor db upload entities.db         # Or specify path
corp-extractor db upload --no-lite           # Skip lite version
corp-extractor db upload --no-compress       # Skip compressed versions

# Download pre-built database (lite version by default)
corp-extractor db download                   # Lite version (smaller, faster)
corp-extractor db download --full            # Full version with all metadata

# Local database management
corp-extractor db create-lite entities.db    # Create lite version
corp-extractor db compress entities.db       # Compress with gzip

See COMPANY_DB.md for complete build and publish instructions.

New in v0.7.0: Document Processing

v0.7.0 introduces document-level processing for handling files, URLs, and PDFs with automatic chunking, deduplication, and citation tracking.

Document CLI

# Process local files
corp-extractor document process article.txt
corp-extractor document process report.txt --title "Annual Report" --year 2024

# Process URLs (web pages and PDFs)
corp-extractor document process https://example.com/article
corp-extractor document process https://example.com/report.pdf --use-ocr

# Configure chunking
corp-extractor document process article.txt --max-tokens 500 --overlap 50

# Preview chunking without extraction
corp-extractor document chunk article.txt --max-tokens 500

Document Python API

from statement_extractor.document import DocumentPipeline, DocumentPipelineConfig, Document
from statement_extractor.models.document import ChunkingConfig

# Configure document processing
config = DocumentPipelineConfig(
    chunking=ChunkingConfig(target_tokens=1000, overlap_tokens=100),
    generate_summary=True,
    deduplicate_across_chunks=True,
)

pipeline = DocumentPipeline(config)

# Process text
document = Document.from_text("Your long document text...", title="My Document")
ctx = pipeline.process(document)

# Process URL (async)
ctx = await pipeline.process_url("https://example.com/article")

# Access results
print(f"Chunks: {ctx.chunk_count}")
print(f"Statements: {ctx.statement_count}")
print(f"Duplicates removed: {ctx.duplicates_removed}")

for stmt in ctx.labeled_statements:
    print(f"{stmt.subject_fqn} --[{stmt.statement.predicate}]--> {stmt.object_fqn}")
    if stmt.citation:
        print(f"  Citation: {stmt.citation}")

PDF Processing

PDFs are automatically parsed using PyMuPDF. For scanned PDFs, use OCR:

# Install OCR dependencies
pip install "corp-extractor[ocr]"

# Process with OCR
corp-extractor document process scanned.pdf --use-ocr

New in v0.4.0: GLiNER2 Integration

v0.4.0 replaces spaCy with GLiNER2 (205M params) for entity recognition and relation extraction. GLiNER2 is a unified model that handles NER, text classification, structured data extraction, and relation extraction with CPU-optimized inference.

Why GLiNER2?

The T5-Gemma model excels at:

• Triple isolation - identifying that a relationship exists
• Coreference resolution - resolving pronouns to named entities

GLiNER2 now handles:

• Entity recognition - refining subject/object boundaries
• Relation extraction - using 324 default predicates across 21 categories
• Entity scoring - scoring how "entity-like" subjects/objects are
• Confidence scoring - real confidence values via include_confidence=True

Default Predicates

GLiNER2 uses 324 predicates organized into 21 categories (ownership, employment, funding, etc.). These are loaded from default_predicates.json and include descriptions and confidence thresholds.

Key features:

• All matches returned - Every matching relation is returned, not just the best one
• Category-based extraction - Iterates through categories to stay under GLiNER2's ~25 label limit
• Custom predicate files - Provide your own JSON file with custom predicates

Extraction Modes

Mode 1: Default Predicates (recommended)

from statement_extractor import extract_statements

# Uses 324 built-in predicates automatically
result = extract_statements("John works for Apple Inc. in Cupertino.")
# Returns ALL matching relations

Mode 2: Custom Predicate List

from statement_extractor import extract_statements, ExtractionOptions

options = ExtractionOptions(predicates=["works_for", "founded", "acquired", "headquartered_in"])
result = extract_statements("John works for Apple Inc. in Cupertino.", options)

Or via CLI:

corp-extractor "John works for Apple Inc." --predicates "works_for,founded,acquired"

Mode 3: Custom Predicate File

from statement_extractor.pipeline import ExtractionPipeline, PipelineConfig

config = PipelineConfig(
    extractor_options={"predicates_file": "/path/to/custom_predicates.json"}
)
pipeline = ExtractionPipeline(config)
ctx = pipeline.process("John works for Apple Inc.")

Or via CLI:

corp-extractor pipeline "John works for Apple Inc." --predicates-file custom_predicates.json

Two Candidate Extraction Methods

For each statement, two candidates are generated and the best is selected:

Method	Description
hybrid	Model subject/object + GLiNER2/extracted predicate
gliner	All components refined by GLiNER2 entity recognition

for stmt in result:
    print(f"{stmt.subject.text} --[{stmt.predicate}]--> {stmt.object.text}")
    print(f"  Method: {stmt.extraction_method}")  # hybrid, gliner, or model
    print(f"  Confidence: {stmt.confidence_score:.2f}")

Combined Quality Scoring

Confidence scores combine semantic similarity and entity recognition:

Component	Weight	Description
Semantic similarity	50%	Cosine similarity between source text and reassembled triple
Subject entity score	25%	How entity-like the subject is (via GLiNER2)
Object entity score	25%	How entity-like the object is (via GLiNER2)

Entity scoring (via GLiNER2):

• Recognized entity with high confidence: 1.0
• Recognized entity with moderate confidence: 0.8
• Partially recognized: 0.6
• Not recognized: 0.2

Extraction Method Tracking

Each statement includes an extraction_method field:

• hybrid - Model subject/object + GLiNER2 predicate
• gliner - All components refined by GLiNER2 entity recognition
• model - All components from T5-Gemma model (only when --no-gliner)

Best Triple Selection

By default, only the highest-scoring triple is kept for each source sentence.

To keep all candidate triples:

options = ExtractionOptions(all_triples=True)
result = extract_statements(text, options)

Or via CLI:

corp-extractor "Your text" --all-triples --verbose

Disable GLiNER2 extraction to use only model output:

options = ExtractionOptions(use_gliner_extraction=False)
result = extract_statements(text, options)

Or via CLI:

corp-extractor "Your text" --no-gliner

Disable Embeddings

options = ExtractionOptions(
    embedding_dedup=False,  # Use exact text matching
    merge_beams=False,      # Select single best beam
)
result = extract_statements(text, options)

Output Formats

from statement_extractor import (
    extract_statements,
    extract_statements_as_json,
    extract_statements_as_xml,
    extract_statements_as_dict,
)

# Pydantic models (default)
result = extract_statements(text)

# JSON string
json_output = extract_statements_as_json(text)

# Raw XML (model's native format)
xml_output = extract_statements_as_xml(text)

# Python dictionary
dict_output = extract_statements_as_dict(text)

Batch Processing

from statement_extractor import StatementExtractor

extractor = StatementExtractor(device="cuda")  # or "mps" (Apple Silicon) or "cpu"

texts = ["Text 1...", "Text 2...", "Text 3..."]
for text in texts:
    result = extractor.extract(text)
    print(f"Found {len(result)} statements")

Entity Types

Type	Description	Example
ORG	Organizations	Apple Inc., United Nations
PERSON	People	Tim Cook, Elon Musk
GPE	Geopolitical entities	USA, California, Paris
LOC	Non-GPE locations	Mount Everest, Pacific Ocean
PRODUCT	Products	iPhone, Model S
EVENT	Events	World Cup, CES 2024
WORK_OF_ART	Creative works	Mona Lisa, Game of Thrones
LAW	Legal documents	GDPR, Clean Air Act
DATE	Dates	2024, January 15
MONEY	Monetary values	$50 million, €100
PERCENT	Percentages	25%, 0.5%
QUANTITY	Quantities	500 employees, 1.5 tons
UNKNOWN	Unrecognized	(fallback)

How It Works

This library uses the T5-Gemma 2 statement extraction model with Diverse Beam Search (Vijayakumar et al., 2016):

1. Diverse Beam Search: Generates 4+ candidate outputs using beam groups with diversity penalty
2. Quality Scoring: Each triple scored via semantic similarity + GLiNER2 entity recognition
3. Beam Merging: Top beams combined for better coverage
4. Embedding Dedup: Semantic similarity removes near-duplicate predicates
5. Predicate Normalization: Optional taxonomy matching via embeddings
6. Contextualized Matching: Full statement context used for canonicalization and dedup
7. Entity Type Merging: UNKNOWN types merged with specific types during dedup
8. Reversal Detection: Subject-object reversals detected and corrected via embedding comparison
9. GLiNER2 Extraction (v0.4.0): Entity recognition and relation extraction for improved accuracy

Requirements

• Python 3.10+
• PyTorch 2.0+
• Transformers 5.0+
• Pydantic 2.0+
• sentence-transformers 2.2+
• GLiNER2 (model downloaded automatically on first use)
• ~2GB VRAM (GPU) or ~4GB RAM (CPU)

Links

• Model on HuggingFace
• Web Demo
• Diverse Beam Search Paper
• Corp-o-Rate

License

MIT License - see LICENSE file for details.