Google Knowledge Graph Explorer

Free, no signup. Look up an entity by name or machine ID and inspect the labels, descriptions, types, and identifiers returned by Google's Knowledge Graph Search API.

Google Knowledge Graph IDs
IDMeaningUse
/m/… Freebase-era machine IDLook up an entity by ID
/g/… Newer Google-native machine IDLook up an entity by ID

Source: Google Knowledge Graph Search API conventions

live Google Knowledge Graph data · top 3 matches

Checks run from our server; we fetch the URL you enter and don't keep the results. 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.

Feedback
Report a bug

Found something broken in Kg Explorer? Let us know what happened — this goes straight to a private triage queue, not a public list.

What will be sent
 No tool inputs, uploads, pasted source, complete results, query parameters, or URL fragments are attached automatically. You can edit or remove the selected passage above. Browser and anti-abuse metadata is processed for spam prevention. 

Data from the Google Knowledge Graph Search API. The confidence number is how well an entity matched your query — it is not an authority or "entity strength" score, and Google is phasing it out. Descriptions come from Wikipedia where shown (licensed CC BY-SA). Results are cached for up to 24 hours.

Sample report

Look up Anthropic, and the tool asks the Knowledge Graph API and returns:

Illustrative example — a live lookup calls Google's Knowledge Graph API, which this static page can't call; the card shape below matches exactly what the tool renders for a found entity

In the Knowledge Graph

Google knows “Anthropic” as an entity.

Anthropic

Technology company

Anthropic is an American AI startup company founded in 2021. Wikipedia →

Organization Corporation Thing
KG ID: /g/11h_2zs0j3 see the knowledge panel → match confidence 812
  • The green banner is the headline — it confirms Google's Knowledge Graph has an entity matching the query at all, before you look at any detail. See what "found" vs. "not found" mean ↓.
  • The mid is the durable identifier/g/11h_2zs0j3 is the machine ID behind this entity's knowledge panel; it's what you'd cite if corroborating the entity elsewhere.
  • Match confidence isn't a percentage — it's a relative relevance score for how well the query matched this entity versus other candidates, useful mainly for comparing results within one search, not across different searches.

Below the card, the tool generates copy-paste JSON-LD that reinforces the entity on a page you own:

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "@id": "https://site.example/#entity",
  "url": "https://site.example",
  "name": "Anthropic",
  "description": "Anthropic is an American AI startup company...",
  "sameAs": [
    "https://en.wikipedia.org/wiki/Anthropic",
    "https://www.google.com/search?kgmid=/g/11h_2zs0j3"
  ]
}

Paste that inside a <script type="application/ld+json"> tag on the entity's home page. The sameAs array links the Wikipedia source and the Google KG ID, so your page corroborates the exact entity Google already knows — the same technique covered in Knowledge Graph SEO.

How to use it

  1. Type an entity name into the box — a person, company, or brand, e.g. Anthropic or Patrick Stox. Use the exact name you'd expect Google to know it by.
  2. Optionally narrow the type filter (Person, Organization, Place, Product…) if a name is ambiguous and you want to disambiguate the match.
  3. Press Look up. You get a found / not-found verdict and up to the top three matching entities.
  4. If it's found, scroll to the schema block, add your site URL, and copy the generated JSON-LD. If it's not found, work through the playbook.
  5. Switch to Compare 2 to look up two entities side by side — useful for checking yourself against a competitor. Use Copy share link to send a lookup to a colleague.

How to read the result

  • In the Knowledge Graph (green) — at least one entity matched your query. The top match is highlighted; up to three matches are shown in case the name is ambiguous.
  • Not found (red) — nothing matched, so the entity isn't in the Knowledge Graph yet. You get the not-found playbook instead of an entity card.
  • Type chips — the schema.org types Google assigns the entity (Organization, Person, Place…). Thing is the catch-all and is dropped from the generated JSON-LD unless it's all Google has.
  • KG ID (mid) — the stable machine identifier, e.g. /m/0d6lp. The "see the knowledge panel" link opens google.com/search?kgmid=… for that exact entity.
  • Match confidence — how well the entity matched your query text, not an authority or "entity strength" score. It's absent for entities returned by the newer API, and Google is phasing it out.

How it works

Each lookup calls a small server endpoint (/api/kg-lookup) that queries the official Google Knowledge Graph Search API with your name and any type filter. The raw response is normalised down to the fields shown — name, mid, types, description, the Wikipedia summary, image, and the match score — and cached for up to 24 hours (misses are cached too, so repeat "not found" checks are cheap).

The JSON-LD is built in your browser from that normalised entity: it picks the most specific schema.org type Google reported, keeps the name and description, and assembles a sameAs array from the Wikipedia URL and the Google KG ID. Add your own site URL and it sets url and an @id so you claim the entity's home page as yours. Nothing you look up is stored; descriptions shown come from Wikipedia (CC BY-SA).

Features

  • Found / not-found verdict against live Google Knowledge Graph data, top three matches.
  • Optional entity-type filter (Person, Organization, Corporation, Place, Product, and more) to disambiguate common names.
  • Entity card with description, Wikipedia summary, type chips, image, and the Knowledge Graph ID plus a direct link to its knowledge panel.
  • Copy-paste schema.org JSON-LD generated for the entity, with an optional site URL to claim url/@id.
  • A concrete not-found playbook for getting an entity recognised.
  • Side-by-side comparison of two entities and shareable links for any lookup or comparison.

Limitations

This checks Google's Knowledge Graph Search API — not the live knowledge panel, which can differ and isn't publicly queryable. A match means Google recognises the entity string, not that a knowledge panel renders for it. The free comparison covers two entities at a time. The match confidence is a query-relevance number, not authority, so don't read it as entity strength. And Google does not publish a guaranteed Knowledge Graph update timeline, so a recent change may not appear in this API and this tool cannot predict when it will.

Frequently asked questions

What is Google's Knowledge Graph?

The Knowledge GraphThe Knowledge Graph is Google's database of entities — people, places, organizations, and things — and the factual relationships between them. It's separate from any single website's structured data: your schema markup is one of many possible inputs to the graph, not the graph itself. is Google's database of real-world 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. — people, companies, brands, places, books, and so on — and the facts and relationships between them. It's what powers the knowledge panel that appears on the right of some search results. When Google recognises your brand or name as an entity, it can attach a machine ID (an mid) to it and understand mentions of you across the web as being about the same thing.

How do I check if my brand is in the Knowledge Graph?

Type the brand or person name into this tool and press Look up. It queries the official Google Knowledge Graph Search API and shows the top matches. If your entity is there you'll see its name, description, types, and Knowledge Graph ID; if nothing matches your name, it isn't in the Knowledge Graph yet and you get a step-by-step playbook for getting in.

What is the match confidence score and is it my entity strength?

No. The confidence number is Google's resultScore — how well an entity matched the text of your query, not how authoritative or well-established the entity is. A common brand name can score high simply because the string matches. Google is phasing this value out of the newer API, and this tool only ever labels it 'match confidence' for that reason. There is no public 'entity strength' score.

What is a Knowledge Graph ID (mid) and where do I use it?

The mid is the stable machine identifier Google assigns an entity, shown in the form /m/0d6lp or /g/11.... It uniquely identifies the entity even if the name is ambiguous. You can view the entity's knowledge panel by searching google.com/search?kgmid=<mid>, and reference it in your schema markupSchema markup is code that uses the schema.org vocabulary to label what your content means so search engines can understand it and show rich results. It's most often written in JSON-LD, and it's not a direct ranking factor. via sameAs so you corroborate which real-world entity your page is about.

My entity isn't in the Knowledge Graph — how do I get in?

Use a consistent, accurate name; publish a clear page about the entity; add appropriate Organization or Person markup; and link to authoritative profiles you control. Google does not publish a guaranteed source list or update timeline for Knowledge Graph inclusion, so treat these as identity-consistency practices rather than an admission checklist.

Local data

Saved targets, named lists, and recent check summaries remain only in this browser.

Next stepEntity Coverage Analyzer — generate the corrected version.

Feature requests for Kg Explorer

Upvote what you want most. New ideas can be submitted from the floating Feedback menu; requests appear here once approved, and the most-wanted rise to the top.

Loading…

➕ Request a feature

New requests are reviewed before they appear here.

Where this tool helps

Common use cases

Check whether Google recognizes an entity

Search for a person, company, or brand and inspect returned names, types, descriptions, and machine IDs.

Compare two possible entities

Place two results side by side to distinguish similar names or evaluate which entity matches the intended subject.

Prepare entity markup

Use a matching result to create copy-ready schema.org JSON-LD while verifying every fact before publishing.

Plan work for an unrecognized brand

Follow the step-by-step playbook when no suitable Knowledge Graph result appears instead of forcing a false match.

Watch the full workflow

Google Knowledge Graph Explorer walkthrough

Read the transcript

Google Knowledge Graph Explorer

If Google recognizes a person, company, or brand as an entity, this tool exposes the matching Knowledge Graph record. I’ll show you the lookup, practical use cases, how to read the result, schema and comparison features, limitations, and the next checks to make.

Step 1

Google’s Knowledge Graph stores records for real-world entities and gives each one a stable machine identifier. This explorer queries the official Search A-P-I for names or I-Ds and shows the returned labels, descriptions, types, and identifiers. It checks the data source, not whether a live knowledge panel currently appears.

Step 2

Use it to disambiguate a common name, capture a stable entity I-D for research, confirm the type Google returns, build a reviewed schema starting point, investigate a missing brand entity, or compare two candidate records. It is especially useful before changing identity markup or same-As links.

Step 3

Choose a preset or type the exact person, company, brand, or machine I-D you expect Google to know. Add an entity-type filter when the name is ambiguous, then select "Look up". A live request returns as many as three matches, so the top result still needs identity review.

Step 4

The illustrative example begins with a green "In the Knowledge Graph" verdict for Anthropic. That means at least one record matched the query. The sample reproduces the live card shape but is clearly teaching data, not a fresh A-P-I response captured during this video.

Step 5

Review the name, description, source summary, and type chips together. The durable value is the Knowledge Graph I-D, shown here as a slash-g identifier. It distinguishes this record from another entity with the same name and can open a search for the exact record. Confirm that every fact describes the intended entity.

Step 6

The match-confidence number is query relevance, not a percentage, authority score, entity strength, or likelihood of a panel. It can help compare candidates within one search, but not different entities across unrelated searches. Google is phasing it out, so a newer record may return no score at all.

Step 7

The generated JSON-L-D is a starting point for an entity home page. It uses a specific schema type, a stable at-I-D on the site you control, the entity name and description, and same-As references for corroborating sources. Verify every value and include only profiles that truly represent and are controlled by the same entity.

Step 8

A red not-found result means this query returned no matching record at that time. It does not create a guaranteed admission checklist. The playbook focuses on one consistent canonical name, a clear entity page, accurate Organization or Person markup, verified same-As links, legitimate corroboration, and patient rechecking without an invented deadline.

Step 9

Switch to "Compare 2" to prepare two names side by side. This is useful when similarly named organizations or competitors return different types and identifiers. The free comparison covers exactly two entities; it does not rank their authority, visibility, market position, or likelihood of appearing in search.

Step 10

The main features are found and not-found verdicts, as many as three candidate records, type filtering, descriptions and source summaries, stable machine I-Ds, direct record searches, browser-generated JSON-L-D, a not-found playbook, two-entity comparison, share links, Search Profile guidance, Wikidata same-As discovery, and a site-schema gap handoff.

Step 11

The Search A-P-I can differ from the live knowledge panel and cannot prove that a panel exists, is claimed, or will render for a query. Scores are not authority. Results can be cached for twenty-four hours, comparison is limited to two records, descriptions can be incomplete, and Google publishes no guaranteed source list or update timetable.

Step 12

Save the query, type filter, lookup time, selected record, machine I-D, and supporting facts. Confirm them with the entity owner, update the canonical entity page and markup, validate the JSON-L-D, and check controlled profiles for consistency. Then review the live search result and re-run the A-P-I later as separate evidence.

Corroborate the entity you can prove.

Save the machine I-D and source facts, correct the page you own, and add only verified corroborating profiles. Then recheck the API and the live search result separately, without treating inclusion or a score as a guarantee of a knowledge panel.