Entity Extraction & Knowledge Graph Construction for Agents
Agent Intelligence Layer

Entity Extraction
& Knowledge Graph
Construction

How autonomous agents parse unstructured text, resolve entities, and weave them into a living graph of interconnected knowledge.

NLP ยท NER Graph Databases RAG Pipelines Agentic AI

What is Entity Extraction?

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Named Entity Recognition (NER)

Identifies and classifies named mentions in raw text โ€” people, places, organisations, dates, products โ€” into pre-defined semantic categories.

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Relation Extraction

Detects typed relationships between entity pairs: founded_by, located_in, acquired. Transforms flat text into structured semantic triples.

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Coreference Resolution

Chains all mentions that refer to the same real-world entity (pronouns, aliases, acronyms) to produce a unified, deduplicated entity set.

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Entity Linking

Maps extracted surface forms to canonical entries in a knowledge base (Wikidata, DBpedia) providing rich, standardised attributes for free.

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Attribute Extraction

Pulls structured properties โ€” dates, quantities, roles โ€” directly from text and attaches them as typed attributes on graph nodes.

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Event Extraction

Captures temporally-grounded events with participants, locations and timestamps, enabling reasoning over what happened, when, and to whom.

Common Entity Types

PERSON
Alan Turing ยท Ada Lovelace ยท CEO ยท Dr. Smith
ORGANISATION
OpenAI ยท UN ยท MIT ยท Acme Corp
LOCATION
London ยท Pacific Ocean ยท Silicon Valley
DATE / TIME
14 March 2024 ยท Q3 ยท last Tuesday
PRODUCT
GPT-4 ยท iPhone 16 ยท Model S
CONCEPT
transformer ยท backpropagation ยท RAG
MONEY
$1.2B ยท โ‚ฌ50M ยท ยฅ3 trillion
EVENT
NeurIPS 2024 ยท IPO ยท merger

Extraction Pipeline for Agents

01

Document Ingestion & Chunking

Raw documents (PDFs, HTML, transcripts) are segmented into context-aware chunks preserving sentence boundaries. Metadata (source, timestamp, author) is attached at this stage.

02

LLM-Powered NER & Relation Extraction

Each chunk is passed to a fine-tuned LLM with a structured JSON output schema. The model simultaneously identifies entities and their typed relationships in a single forward pass.

03

Coreference & Deduplication

A resolution step clusters surface-form variants of the same entity (e.g., “Sam Altman”, “he”, “the CEO”) into a single canonical node, preventing graph fragmentation.

04

Knowledge Base Linking & Enrichment

Canonical entities are disambiguated against Wikidata / internal KBs. Matched entries inherit rich structured properties (aliases, types, geographic coordinates) automatically.

05

Graph Upsert & Indexing

Entities become nodes; relations become typed, weighted edges. Provenance edges link each fact back to its source chunk โ€” enabling citation-grounded retrieval and auditability.

06

Agent Query Interface

Agents traverse the graph via Cypher / SPARQL, vector-search over node embeddings, or hybrid retrieval โ€” combining structured graph paths with semantic similarity scores.

Knowledge Graph Anatomy

works_at born_in authored mentions attended published_by cited_in located_in part_of PERSON Alan Turing ORG GCHQ PLACE London CONCEPT Turing Test DOCUMENT Paper 1950 EVENT WWII ORG Nature CONCEPT Computing PLACE UK ORG ACM

LLM Extraction Prompt Schema

extract_entities.py
# Entity + Relation extraction with structured output

SYSTEM_PROMPT = """
You are a knowledge graph construction agent.
Given a text passage, extract ALL entities and relations.

Return ONLY valid JSON matching this schema:
{
  "entities": [
    { "id": "e1", "text": "Alan Turing", "type": "PERSON",
      "canonical": "Q7251", "confidence": 0.97 }
  ],
  "relations": [
    { "subject": "e1", "predicate": "works_at",
      "object": "e2", "confidence": 0.91 }
  ]
}
"""

async def extract_graph_from_chunk(chunk: str) -> dict:
    response = await llm.complete(
        system=SYSTEM_PROMPT,
        user=f"Extract entities and relations:\n\n{chunk}",
        temperature=0.0,    # deterministic
        response_format="json_object"
    )
    data = json.loads(response.text)
    return resolve_and_upsert(data)   # dedup + write to graph

How Agents Use the Graph

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Graph Traversal (Cypher)

Agents issue structured graph queries to follow multi-hop relation chains โ€” e.g., “find all organisations connected to Person X within 2 hops.”

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Vector + Graph Hybrid RAG

Semantic search over node embeddings seeds initial retrieval; graph traversal then expands context by following typed edges to related nodes.

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Provenance & Citations

Every edge carries a source_chunk_id. Agents can cite the exact document and passage that grounded each stated fact โ€” eliminating hallucination risk.

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Incremental Updates

New documents trigger partial re-extraction. Conflict-detection merges contradictory facts and flags nodes with temporal version annotations for agent review.

Benefits for Agentic Systems

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Precision Retrieval

Typed paths guarantee topically relevant context; irrelevant chunks are never retrieved, slashing noise and reducing token cost dramatically.

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Compositional Reasoning

Multi-hop questions that stump flat-vector RAG systems are solved by following graph edges across conceptual distances without re-reading source text.

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Auditable & Grounded

Every agent claim traces back to a source node, a source edge, and a source document. Compliance and explainability are first-class citizens.

Entity Extraction & Knowledge Graph Construction for Agents  ยท  Built with care

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