Compare Ambiguous Wikidata Entity Matches

Ambiguous names are normal in a knowledge graph. “Mercury” can identify a planet, element, deity, vehicle, newspaper, or company. A reliable resolver must compare candidates instead of assuming search order equals truth. The Wikidata Entity Scraper exposes enough context for that review.

Request multiple candidates

Use a modest per-query limit and preserve match context.

{"queries":["Mercury","Jaguar","Apple"],"language":"en","maxResultsPerQuery":10}

Build a comparison table with Q-ID, label, description, aliases, matched type, relevant claims, statement count, sitelink count, and source URL. Description usually provides the fastest category check. Claims can then confirm type, country, parent organization, dates, or other source attributes.

Do not score candidates only by statement or sitelink count. Popular entities naturally have richer graphs, which can bias matches away from the correct obscure entity. Define rules based on the input record: expected entity type, known website, location, date, or external identifier. Require manual review when decisive evidence is absent.

Save approved Q-IDs with the matching evidence and reviewer decision. Recurring enrichment should fetch exact IDs rather than repeat search. Keep rejected candidates if auditability matters.

Wikidata content can change, and search ranking is not a contract. Conservative unresolved outcomes protect data quality. A missing enrichment is visible and repairable; a confidently attached wrong entity can silently corrupt analytics, recommendations, and published content.

Retest matching rules across people, places, organizations, creative works, and products because evidence that separates one entity class may fail badly for another.