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Entity & Schema Generator

Identify the organization, people, products/services and topics a page is about, see which schema.org types are missing, and get ready-to-paste JSON-LD generated from the page's own content — the structured signal that helps search engines and AI assistants cite you correctly.

⚡ Interactive demo — sample data
Found 4 entities. 2 of 3 recommended schema types are missing — ready-to-deploy JSON-LD generated below.
Aurora Dental Group — a multi-location dental practice
Missing — recommended JSON-LD generated Issue
Dr. Lena Ortiz — lead dentist named on the About page
Missing — page has 5 visible Q&A pairs with no FAQPage schema Issue
Already present in existing JSON-LD Looks good

About this tool

Identify the organization, people, products/services and topics a page is about, see which schema.org types are missing, and get ready-to-paste JSON-LD generated from the page's own content — the structured signal that helps search engines and AI assistants cite you correctly.

How it works

Enter your page URL
Paste any public URL and run the check. We fetch the page and read its title, meta description, H1, and visible text, plus every JSON-LD structured-data block already published on it.
We identify the entities and the schema gaps
The tool works out who and what the page is actually about — the organization or brand, named people, products or services, the location, and the key topics — then compares that against your existing JSON-LD to see which schema.org types are missing.
Copy the generated JSON-LD into your <head>
For every recommended type that isn't already on the page, you get a ready-to-paste JSON-LD <script> block populated from what the page actually says — nothing invented. Drop it into your template and re-run to confirm the gap is closed.

What we check

  • Entity identification — Extracts the real-world entities the page is about: the organization/brand, named people, products or services, physical location, and the handful of topics the content is actually centered on.
  • Knowledge-graph linkage — For entities that are well-known, unambiguous public entities (a real company, a recognizable person, a well-known place), notes how they could be linked to a knowledge graph — for example a likely Wikidata entry or Wikipedia title — so machine readers have a way to disambiguate who you mean, not just what string you typed.
  • Schema.org type recommendations — Decides which schema.org @types belong on this specific page — Organization, LocalBusiness, Product, Service, FAQPage, BreadcrumbList, Article, Review, Event, and more — based on what the page's content actually supports, not a one-size-fits-all template.
  • Existing JSON-LD inventory — Reads every JSON-LD block already on the page, including nested entities and @graph arrays, and pulls out every @type currently present.
  • Gap analysis — Diffs the recommended types against what's already there so you see, at a glance, which types are already covered and which are missing.
  • Ready-to-deploy JSON-LD generation — For every missing, recommended type, generates a complete JSON-LD object populated with facts extracted from the page — never invented — formatted as copy-paste <script type="application/ld+json"> blocks.

Common issues we catch

  • No structured data at all — A large share of pages ship zero JSON-LD, which means search engines and AI assistants have to infer everything from unstructured HTML — a slower, less reliable path to being understood and cited correctly.
  • Only the homepage has Organization schema — Organization markup on the homepage doesn't help a blog post, product page, or FAQ page assert its own entities. Every page type generally needs its own appropriate schema, not just a single sitewide block.
  • FAQ content with no FAQPage schema — A page can visually look like an FAQ — question headers, answer paragraphs — without a single machine-readable signal that says so. Without FAQPage markup, that content is far less likely to be lifted directly into an AI-generated answer.
  • Ambiguous entity names — A brand name that's also a common word, or a person who shares a name with someone more famous, is hard for a machine reader to disambiguate from text alone. Explicit entity markup — and a knowledge-graph link where one genuinely exists — resolves the ambiguity.
  • Product or service pages with no Product/Service schema — Pages that clearly describe an offering, a price, or a service area often skip the schema.org type that would let that information surface directly in rich results or be quoted precisely by an AI assistant.
  • JSON-LD that's stale or contradicts the visible page — Structured data copy-pasted from a template and never updated can describe an entity that no longer matches the page's actual content — a mismatch that erodes trust with both search engines and AI systems that cross-check the two.

Where this matters

  • Google Knowledge Panels & rich results — Well-formed Organization, LocalBusiness, Product, and FAQPage markup is part of how Google builds Knowledge Panels and qualifies pages for rich results — clear entity signals give it more to work with.
  • ChatGPT, Gemini, Perplexity & Google AI Overviews — AI assistants that answer questions by citing sources rely on being able to confidently identify who or what a page is about. Explicit entity markup and schema.org typing reduce the guesswork that can lead an assistant to cite a competitor's cleaner-marked-up page instead of yours.
  • Wikidata & knowledge-graph tooling — Where an entity on your page corresponds to a real, well-known public entity, a correct knowledge-graph reference helps any system that consumes structured entity data resolve exactly who or what you mean — not just a similar-sounding name.
  • WordPress, Shopify & page builders — Most CMS platforms let you paste raw JSON-LD into a template, theme header, or plugin field — the generated <script> blocks from this tool drop in as-is.

Frequently asked questions

What is entity SEO?
Entity SEO is optimizing a page so search engines and AI systems can confidently identify the specific real-world things it's about — a business, a person, a product — rather than just matching keywords. Structured data (JSON-LD) is the primary way to state those entities explicitly instead of hoping they're inferred correctly.
What's the difference between this and a basic schema generator?
A basic schema generator fills in a fixed template (typically just Organization and WebSite) with values scraped from the page. This tool reads what the page is actually about, decides which schema.org types genuinely fit that content, and only generates markup for the types that are missing and appropriate — informed by your existing JSON-LD, not a one-size-fits-all form.
Will this invent information about my business?
No. Every generated JSON-LD property is populated from facts found on the page itself. If a property can't be confidently sourced from the page's actual content, it's left out rather than guessed.
What is a knowledge-graph linkage note?
When an entity on your page is a well-known, unambiguous public entity — a real company, a recognizable person, a well-known place — the tool notes how it could be connected to a knowledge graph, such as a likely Wikidata entry or Wikipedia title. This is only offered when there's genuine confidence; it's never fabricated.
Which schema.org types can this tool recommend?
It chooses from a curated, well-supported set: Organization, LocalBusiness, Person, Product, Service, WebSite, WebPage, FAQPage, BreadcrumbList, Article, Review, AggregateRating, Event, JobPosting, and HowTo — whichever of those genuinely fit what the page contains.
Do I need to replace my existing JSON-LD?
No. The tool only generates markup for schema types it determines are missing and appropriate. Types you already have are left alone and simply reported as already present.
Where do I put the generated JSON-LD?
Paste each <script type="application/ld+json"> block into the <head> of the page it was generated for (or wherever your CMS/theme accepts custom head markup), then re-run the check to confirm it's picked up.
Does adding schema markup guarantee an AI assistant will cite my page?
No tool can guarantee citation — that depends on many factors including content quality, authority, and what the assistant's underlying search finds. What clear entity markup does is remove ambiguity: it gives any system reading the page a precise, structured answer to "who/what is this page about" instead of forcing it to infer one.

This is one of several free SEO tools from Custom Web Audits. For a complete, prioritized analysis of your whole website, run a full audit.