EntityLab.
Investigate an entity through observable digital evidence: visible Knowledge Graph output, citation context, product and service category associations, related-search neighborhoods and — when supplied — the canonical domain’s ranked category footprint.
Entity evidence is layered.Keep every source distinct.
Knowledge Graph output, citation evidence, category associations and related searches are separate datasets. Entity Lab combines them only after their provenance is preserved.
When Google exposes a panel.Record what is actually visible.
A captured result may contain a structured identity panel with a title, subtitle, description, reference URL, observed position and additional attributes. Each field is recorded separately.
Awaiting Knowledge Graph capture
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The structured Knowledge Graph description will appear here if the live SERP returns the element.
Do not invent relationships.Map observed evidence layers.
The graph is a TAO analytical representation. A line means the signals were observed in the same research capture — not that a hidden search-engine entity edge has been proven.
Entity label versus domain footprint.Compare, do not merge.
Categories for Keywords classifies the entity search label. Categories for Domain reflects categories containing keywords the supplied domain ranks for. They are distinct signals.
Where is the entity cited?Inspect page and semantic context.
Citation records connect the observed URL and domain with page type, category and content-level context such as title, semantic location, quality signal, country and language.
| # | DOMAIN | PAGE / TITLE | PAGE TYPE | TEXT CATEGORY | SEMANTIC LOCATION | QUALITY | COUNTRY | LANGUAGE |
|---|---|---|---|---|---|---|---|---|
NO CITATION CAPTURE | ||||||||
The entity label lives in search context.Related searches are query evidence.
Related Keywords follows Google’s “Searches related to” relationships. It is useful search context, but not proof of an internal entity relationship.
| # | RELATED QUERY | VOLUME | CPC | COMPETITION | INTENT | KD | CATEGORIES |
|---|---|---|---|---|---|---|---|
NO RELATED-QUERY CAPTURE | |||||||
Observable first.Interpretation comes after the evidence.
Identity, citations, categories and search context remain separate research dimensions so the interface stays explainable and reproducible.
Structured SERP fields when Google returns a Knowledge Graph element.
Top domains, page types, categories and content-level citation context.
Observed product and service category associations for the entity label and supplied domain.
Observed “Searches related to” expansion around the entity label.
Six evidence channels.One normalized entity record.
Entity Lab keeps visible identity fields, citations, classifications and related-query context in separate channels. Only comparable observations enter the assembled record, and every signal retains its scope and origin.
ENTITY LABEL → IDENTITY PANEL → OBSERVED FIELDSRecord the title, type, description, reference URL, visible position and additional attributes when an identity panel appears.
ENTITY LABEL → REFERENCING PAGES → CONTEXTPreserve individual citation URLs, domains, page types, categories and the semantic location of each reference.
RECORDS → DOMAINS → CONTEXT DISTRIBUTIONSAggregate citation volume, recurring domains, page types, categories, countries and languages without erasing the underlying records.
ENTITY LABEL → CATEGORY ASSOCIATIONSClassify the searched entity label while keeping query-level categories distinct from the domain’s wider footprint.
CANONICAL DOMAIN → RANKED FOOTPRINT → CATEGORIESUse the optional domain to observe category associations across its ranked query territory.
ENTITY LABEL → RELATED SEARCHES → NEIGHBORHOODExpand the visible query neighborhood without treating related searches as proof of a hidden entity relationship.
Knowledge Graph evidence is one layer of the entity record.
A Knowledge Graph panel is an observable SERP feature, while citation context, category associations and related searches are separate evidence layers. Entity Lab keeps those layers distinct and uses them to build an explicit TAO research record rather than collapsing them into a single opaque score.
Entity Lab FAQ.Evidence before inference.
Twenty-five methodological answers covering Knowledge Graph observations, citations, classification boundaries, graph interpretation and research limitations.
What does Entity Lab investigate?
It separates visible identity information, citation records, category associations and related-search evidence into distinct research channels. The interface does not claim access to Google’s internal Knowledge Graph.
Does a Knowledge Graph panel prove an entity is recognized everywhere?
No. A panel is an observed search-result feature for a particular query, market, language and capture. Its presence does not establish universal recognition across search experiences.
What happens when no Knowledge Graph panel appears?
The identity channel reports that no panel was returned in that capture. Citation, category and related-query channels may still provide useful evidence.
How is an entity name different from a canonical domain?
The entity name is the search label under investigation. The optional domain supplies an independent website footprint for comparison; the two inputs should not be treated as interchangeable.
Why is the canonical domain optional?
Some entities are people, places, products or concepts without a single canonical website. The identity and query-level evidence can be examined without adding domain-level classification.
Which market and language does this interface request?
The supplied implementation sends United States location code 2840 and English language code en. These are fixed request settings in the current interface.
What is a citation record in this lab?
A citation record is a returned page or content reference associated with the searched label. It can include its URL, domain, page type, category, semantic location and other available fields.
Are citation counts the same as backlinks?
No. A content citation or mention does not necessarily contain a clickable hyperlink, and a backlink does not necessarily demonstrate meaningful entity recognition.
What does the top citation domain represent?
It is the leading domain in the returned citation summary, subject to the provider’s coverage and counting rules. It is not necessarily the most authoritative source.
Can the same domain contribute multiple citations?
Yes. Depending on the returned records, multiple pages or references from one domain may appear. Domain totals and individual records should be interpreted separately.
What is semantic location in citation analysis?
It describes where a reference appears within the analyzed content when the data source supplies that field. It is contextual metadata, not a guaranteed search-ranking factor.
Does content quality represent an official Google score?
No. A returned quality field is a provider-specific metric or classification. It should not be described as a Google ranking score without explicit evidence.
What are query categories?
They are category associations returned for the entity search label. They characterize the query context, not the full commercial or semantic footprint of a website.
What are domain categories?
They describe categories associated with keywords ranked by the optional supplied domain. This is a different scope from categorizing a single entity search label.
Why must query and domain categories remain separate?
Combining them can create false equivalence between a searched entity and all topics on a website. Their distinct provenance is essential to sound interpretation.
What do related searches demonstrate?
They expose search-query associations returned by the provider. Related searches do not prove a direct relationship in Google’s internal entity graph.
Can related-query search volume be treated as exact demand?
No. Volume is an estimate with geographic, temporal and provider-specific limitations. It should be read as directional evidence rather than an exact census.
What does keyword difficulty measure here?
It is a provider-reported difficulty metric when available for a related query. It is not a universal probability of ranking or an official search-engine score.
What do the graph lines mean?
They visually connect evidence channels collected in the same research operation. The lines do not assert hidden semantic edges or verified relationships between entities.
Are graph nodes live data before a search is run?
No. The initial labels are interface placeholders. The graph is populated from normalized response fields after a successful research request.
How many citation and related-query records are requested?
The current request specifies a citation limit of 40, a related-query limit of 30 and a category limit of 10. The provider may return fewer records.
Why might results vary between two runs?
Search surfaces and provider datasets can change over time. Geography, language, data freshness, collection method and incomplete coverage can also affect observations.
How should missing fields be interpreted?
A missing field means that value was not supplied or normalized for the current capture. It must not automatically be interpreted as a zero value or proof of absence.
Does Entity Lab certify entity ownership or identity?
No. It investigates observable digital evidence. Legal ownership, organizational control and identity verification require separate authoritative checks.
How should researchers use Entity Lab findings?
Retain the capture context and source-level evidence, compare each channel independently and document uncertainty. Use the results to frame further verification rather than to manufacture a single authority score.
Twelve laboratories.One research system.
SERP Lab
Live SERP, AI Overview, PAA and related-search evidence.
Domain Lab
Domain visibility, ranked keywords, top pages and competitors.
Keyword Lab
Demand, intent, suggestions, related searches and keyword expansion.
Competitor Lab
Shared keywords, intersections, competing pages and gap zones.
AI Visibility Lab
AI mentions, sources, cited pages and query-level visibility.
Entity Lab
Knowledge Graph evidence, citations, categories and related-search context.
Search Intent Lab
Intent classification, SERP composition and alignment.
Internal Linking Lab
Internal graph, crawl depth, anchors and orphan patterns.
Topical Map Lab
Topic nodes, semantic neighborhoods and coverage architecture.
Content Gap Lab
Missing queries, subjects and competitive coverage gaps.
Backlink Lab
Referring domains, citations and external link structure.
Retrieval Lab
Retrieval, grounding, sources and RAG-oriented evidence.