Wikipedia's little sibling answers questions LLMs guess at.

Wikidata holds structured facts for over 100 million entities — people, companies, chemicals, films — each with stable Q-IDs and typed claims. The native SPARQL endpoint and entity API are powerful, but their response shapes are built for machines reading ontologies, not pipelines reading tables.

The Wikidata Extractor wraps both access paths: search by term to resolve names to Q-IDs, or pass exact IDs to pull full records.

{
  "searchTerm": "Ada Lovelace",
  "maxResults": 5
}

{
  "qid": "Q7259",
  "label": "Ada Lovelace",
  "description": "English mathematician …",
  "aliases": ["Augusta Ada King, Countess of Lovelace"],
  "claims": { "P31": "Q5", "P106": ["mathematician", "writer"] }
}

Where it earns its keep

Entity resolution: map messy company names from a CRM to canonical IDs before deduplicating. RAG grounding: give models facts with provenance instead of parametric memory. Disambiguation: "Apple" the company versus "apple" the fruit resolves cleanly by Q-ID.

The honest limit: Wikidata is only as complete as its contributors. Long-tail local businesses won't be there — but anything with a Wikipedia article will.