AI SEO Projects ·Entity Authority
Disambiguation kit for generic-word product names
Last reviewed:
- Owner
- Entity owner, with per-market content leads
- Metric
- Correct entity resolution rate for at-risk product names, per market
- Clock
- Model layer —the two clocks
Technology products often carry generic-word names — common nouns used as product lines — which makes entity disambiguation failures a structural hazard for the category. When a model can’t tell your product from the everyday word, it omits the citation. This project builds the canonical anchors that force resolution.
Why this, mechanically. A model resolves an entity before citing it; if the name collides with common usage, resolution fails silently and the omission never shows up as an error. A dense, schema-backed “what is [product]” page per market gives the model an unambiguous anchor with enough sameAs signal to pick the right entity.
Deliverable. Canonical “what is [product]” pages for the most collision-prone product names, per product per market, each carrying Organization/Product schema and dense sameAs links.
Finish line. Each at-risk product name has a canonical anchor in every priority market, and disambiguation testing shows the model resolving to your entity rather than the common noun.
Background: entity disambiguation in AI search. This is per-market work — it feeds, and is fed by, the multilingual entity parity audit.