Resolve Names to Wikidata Q-IDs

Entity resolution is the step between a text name and a durable knowledge-graph identifier. Selecting the first search result blindly creates subtle errors because the same phrase can describe several entity types. The Wikidata Entity Scraper returns candidate context for a reviewable match.

Generate a small candidate set

Search related names in the language of the source data.

{"queries":["OpenAI","Ada Lovelace","Bengaluru"],"language":"en","maxResultsPerQuery":5}

Use matched_text and matched_type to understand why a result appeared. Compare label and description first, then inspect aliases, high-value claims, Wikipedia link, and sitelink count. Do not use popularity or result order as the only decision rule.

Create a review table with source name, candidate Q-ID, candidate description, decision, reviewer, and reason. Once approved, switch recurring workflows to entityIds. Exact retrieval avoids future search-ranking changes and makes joins deterministic.

Automated matching can apply conservative rules when the input has supporting attributes such as birth date, country, website, or entity type. Ambiguous cases should remain unresolved rather than forced. False matches contaminate every downstream enrichment and are usually more expensive than missing values.

Wikidata is community-maintained, so even an approved entity can change. Keep source URLs and periodically refresh display metadata. Treat Q-IDs as identifiers for graph entities, not certifications that every claim is correct. A careful candidate-and-review process turns public graph search into reliable internal linkage.

Measure resolution quality on a labeled sample before scaling automation, and track false matches separately from records deliberately left unresolved.