| name | extract-entities |
| description | Extract named entities, topics, and relationships from text or structured content |
| type | python |
| flattens_collections | true |
| parameters | none |
| examples | ["{\"type\":\"extract-entities\",\"target\":\"$paper_text\",\"out\":\"$entities\",\"expect\":\"should find authors and organizations\"}"] |
Extract Entities
Identify and extract structured information from unstructured text: people, places, organizations, topics, dates, and relationships between entities.
Purpose
Transform free-form text into structured entity data for:
- Building knowledge graphs
- Indexing and retrieval
- Pattern detection across documents
- Linking related content
Input Format
Accepts:
- Plain text (paragraphs, documents)
- Structured data with text fields
- Lists of text snippets
Output Format
Returns JSON structure:
{
"people": ["Name1", "Name2"],
"organizations": ["Org1", "Org2"],
"locations": ["Place1", "Place2"],
"topics": ["Topic1", "Topic2"],
"dates": ["2025-01-15", "last week"],
"key_concepts": ["Concept1", "Concept2"],
"relationships": [
{"subject": "Name1", "predicate":
Extraction Guidelines
Entity Categories
People: Full names, roles, pronouns with clear referents
- Include professional titles if mentioned
- Resolve pronouns when unambiguous
Organizations: Companies, institutions, projects, teams
- Include both formal and informal names
- Note parent/subsidiary relationships
Locations: Cities, countries, venues, virtual spaces
- Be specific when possible (not just "the office")
Topics: Domain areas, technologies, methodologies
- Extract at appropriate granularity (not too broad/narrow)
- Include synonyms if multiple terms used
Dates/Time: Absolute and relative temporal references
- Normalize when possible (ISO format for absolute dates)
- Preserve relative references ("next week", "recently")
Key Concepts: Abstract ideas, themes, goals
- Focus on novel or emphasized concepts
- Distinguish from general background
Relationships
Extract explicit and strongly implied relationships:
- Employment/affiliation
- Collaboration/partnership
- Causation/dependency
- Temporal ordering
- Hierarchical structure
Format: {subject, predicate, object} triples
Quality Standards
- Precision over recall: Only extract clear, confident entities
- Disambiguation: Use context to resolve ambiguous references
- Normalization: Consistent entity naming across text
- No hallucination: Never infer entities not present in source
Special Handling
Pronouns: Resolve only when antecedent is clear and recent
Abbreviations: Expand on first use, preserve thereafter
Implicit entities: Extract only if strongly implied by context
Conflicting info: Note conflicts in relationships field
Parameters
Optional args dict can specify:
entity_types: List of types to extract (default: all)include_confidence: Boolean, add confidence scores (default: false)max_entities_per_type: Limit results (default: unlimited)
Example
Input:
Sarah joined Anthropic last quarter to work on constitutional AI.
She previously collaborated with researchers at DeepMind on alignment.
Output:
{
"people": ["Sarah"],
"organizations": ["Anthropic", "DeepMind"],
"topics": ["constitutional AI", "alignment"],
"dates": ["last quarter"],
"key_concepts": ["alignment research"],
"relationships": [
{"subject": "Sarah", "predicate": "works_at", "object": "Anthropic"},
{"subject": "Sarah", "predicate":
