Wikidata items with sameAs, founded, founder, and industry fields are cited at nearly double the rate.
A predictive model trained on 6 months of citation data from GPT, Claude, Perplexity, and Gemini finds that Wikidata entity completeness predicts citation probability with 78% accuracy. The four most predictive fields: sameAs links to external authoritative sources, foundingDate, founder (linked entity), and industry classification.
Brands with complete Wikidata entities — all four fields present and linked — are cited at 1.9x the rate of brands with partial or missing Wikidata coverage. The effect is strongest for Perplexity (2.1x) and Gemini (2.0x), slightly lower for Claude (1.7x) and GPT (1.6x).
The mechanism is clear: LLMs use Wikidata as a ground truth for entity disambiguation. When a query includes a brand name, the model resolves it to a Wikidata entity. If the entity has rich, verified attributes, the model has more material to generate a substantive citation.
*What this means:* Wikidata entity management is now a core component of citation infrastructure. Create the entity if it doesn't exist; complete it if it's partial; maintain it as your company grows. The Wikidata entity graph playbook covers every property that matters, or scan your domain to get a baseline on your current entity coverage.
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