Entity Coverage Analyzer
Free, no signup. Find likely entities in your page text, then reconcile selected names against Google’s Knowledge Graph and Wikidata. Local extraction works when the AI path is unavailable.
Runs entirely in your browser — nothing you paste is uploaded or stored. The optional AI extraction sends your pasted text to the bounded model endpoint. Reconciliation sends only a selected entity name to Google KG and Wikidata; local extraction stays in your browser. Anonymous run-level outcome counters may be used for aggregate research; URLs, domains, IPs, and identifiers are never included, and no statistic is released below 100 runs.
Sample report Deterministic local example
For “Cloudflare operates Workers AI. Cloudflare provides services to Acme Analytics.”, local extraction returns:
Cloudflare · 2 mentions · heuristic
Workers AI · 1 mention · heuristic
Acme Analytics · 1 mention · heuristic
No sameAs links appear in the JSON-LD until a user performs a lookup and accepts an identifier. The example intentionally stops before reconciliation because a real match depends on the live service and human identity review.
How to use it
- Paste the visible text or HTML from one page.
- Choose Extract entities for the local capitalized-name heuristic, or the optional model-assisted extraction.
- Review the proposed names and select Reconcile IDs only for entities you intend to check against Google KG and Wikidata.
- Inspect ambiguous results before using the generated
aboutandmentionsJSON-LD.
Site passport Local context for this saved site
Local data
Saved targets, named lists, and recent check summaries remain only in this browser.
Rate this tool
Entity JSON-LD
What you get
- heuristic — proposed by the local capitalized-name pattern.
- observed — parsed from the point-in-time model-assisted response, not independently verified.
- linked — a lookup returned a stable identifier; confirm the identity manually.
- ambiguous — more than one candidate was returned and the first must not be assumed correct.
How it works
The local path counts capitalized sequences of up to five terms, removes a small stop list, and sorts by mentions. The optional AI path asks the bounded endpoint for candidate names. Google KG and Wikidata lookups are separate and user-triggered. Generated WebPage JSON-LD includes only linked entities: the first under about, subsequent matches under mentions, and the reconciled KG, Wikidata, Wikipedia, and claimed social URLs under sameAs.
Features
- Local extraction that works without an API or model.
- Optional model-assisted candidate extraction with visible provenance.
- Per-entity, on-demand Google KG and Wikidata reconciliation.
- Richer
sameAsJSON-LD restricted to reconciled entities. - Explicit ambiguity and unavailable-result states.
Limitations
The heuristic misses lowercase brands and common-noun concepts, and can mistake headings or sentence openings for entities. Mention count is not topical importance. Model extraction can vary. The first Knowledge Graph result is not necessarily the intended entity, and the generated ordering is not a semantic judgment. This tool does not compare the page against a required topic model or competitors.
Compare existing entity schema
Paste an existing Person or Organization node to find missing name, URL, description, and identity links after reconciliation.
Frequently asked questions
What is entity coverage?
Here it means the named people, organizations, products, and concepts that appear in the supplied page text. The tool inventories likely names; it does not decide which entities"Entity & identity schema" is a practitioner label — not an official Google or schema.org category, and not exhaustive of identity-capable schema.org types — for the three main types that declare who or what is behind a site: Organization, LocalBusiness, and Person. a topic must include.
What is the difference between extraction and reconciliation?
Extraction proposes names found in text. Reconciliation connects a selected name to a stable knowledge-graph identifier. A name can be extracted correctly but reconciled to the wrong real-world entity, so every match needs review.
Does a Knowledge Graph miss mean an entity does not exist?
No. A miss can reflect ambiguity, coverage, quota, or service availability. The tool reports it as unmatched or not evaluated, never as proof of nonexistence.
Can I publish the generated JSON-LD as-is?
Review it first. Only reconciled entities are included, but the top match can still be wrong. Confirm each identity and ensure the entity is genuinely about or mentioned by the page.
Does adding more entities improve AI visibility?
Not automatically. Clear, accurate coverage can reduce ambiguity, but stuffing names or schema that the visible page does not support creates misleading markup rather than authority.
Feature requests for Entity Coverage Analyzer
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Common issues & how to fix them
- Warnings Likely entity has not been reconciled Fix: Review candidate identities and attach the correct stable Knowledge Graph ID before generating entity references.
- Warnings Important expected entity is absent Fix: Add accurate coverage of the expected entity where it materially helps answer the page’s topic.
- Warnings Entity candidate maps to multiple identities Fix: Disambiguate the name with type and context, then select the verified entity ID rather than the first match.
- Information Entity coverage is sparse Fix: Add missing topic-critical entities and relationships based on the page’s actual subject, not raw mention counts.