Deep audits
Entity SEO + Wikidata matching
1 min readUpdated May 22, 2026
What the Entity SEO audit does
/dashboard/sites/<id>/audit/entities extracts named entities from every page, classifies them, and matches them against Wikidata's knowledge graph.
Extraction modes
- Local (all tiers) β regex-based NER for capitalised multi-word names with particle-tolerant pattern (handles "de la", "van der", "von" prefixes), classified into Person / Organization / Place / Product / Brand / Event / WorkOfArt / Other via ORG_SUFFIXES, PERSON_TITLES, and PLACE_HINTS dictionaries
- AI-enhanced (Agency+) β Sonnet 4.6 strict-JSON extraction with top-40 cap and β₯2-occurrence floor
Wikidata matching
For each entity with β₯3 occurrences, we run a SPARQL query against Wikidata's public endpoint with an exact/substring/fuzzy confidence ladder (0.95 / 0.75 / 0.5). Matched entities surface their Q-ID + sameAs URL list (Wikipedia, official sites, social profiles).
What this gets you
- Is your client's site itself in Wikidata? This is a heavy AI Search ranking factor β LLMs (ChatGPT, Perplexity, Gemini) preferentially cite Wikidata-indexed entities
- Match rate of entities mentioned on your site β gives you a "knowledge graph fluency" signal
sameAscoverage β how complete your schema's external-link declarations are- Manual Q-ID override (Agency+) β bind a specific entity to a specific Q-ID if our auto-match misses it
Was this article helpful?
Still stuck? Our support team replies within 8 hours on weekdays.
More in Deep audits