Does ChatGPT crawl my site the same way Google does?

Not exactly the same way. ChatGPT (via its built-in browsing or search plugins) can consult web pages in real time depending on the conversation's context, but its base model was also trained on a large text corpus collected at a given point in time. Getting cited therefore depends both on your content's historical presence in training corpora and on the model's ability to retrieve and verify up-to-date information during a live search.

What is entity-based SEO?

Entity-based SEO means describing your brand, products, and expertise as clearly defined entities linked to one another - via Schema markup and strict terminology consistency - rather than as free-form marketing copy. Language models understand and cite a named entity with structured attributes far more easily than an ambiguous descriptive paragraph.

Does Schema markup guarantee I'll be cited by ChatGPT?

No, no technique guarantees a citation. Schema markup significantly increases the odds that a generative engine can confidently extract a verifiable fact from your site, but the final citation also depends on the domain's perceived authority, competition on the topic, and the exact context of the query posed to the model.

Do I need a dedicated data feed for AI (LLM citation feed)?

It isn't strictly required, but a structured, up-to-date feed - for example a centralized JSON-LD file or a 'verified facts' page clearly listing your company data, statistics, and product information - makes extraction easier for any generative engine visiting your site, whether that's ChatGPT, Perplexity, or a future equivalent system.

Back to Blog

chatgpt entity-based-seo schema-markup geo

How to Get Cited by ChatGPT: Schema Markup, Entity-Based SEO, and LLM Citation

R Runkexpert Engineering Team · July 15, 2026

Runkexpert graphic showing a site with structured Schema markup being cited as a source in a ChatGPT answer

Getting cited in a ChatGPT answer is not a matter of luck: it’s the direct result of how your site presents its facts. A language model doesn’t “pick” a source at random - it extracts the information it can verify and attribute with the most confidence, and poorly structured content is simply harder to cleanly extract than content that’s tagged and unambiguous.

Why Some Sites Get Cited and Others Don’t

Two sites can cover exactly the same topic, with comparable writing quality, and yet only one gets cited by ChatGPT. The difference is rarely the content itself - it’s how that content is structured for extraction.

A language model processes free text by trying to identify entities, attributes, and the relationships between them. A paragraph that buries a precise fact (“Runkexpert offers technical SEO audits based on 3 live-measured Core Web Vitals metrics”) under three sentences of marketing context makes that extraction harder than an Organization or FAQPage Schema markup exposing that same fact in a structured, unambiguous way.

Schema Markup: The Language AI Understands Best

JSON-LD markup remains, as of today, the most reliable format for conveying verifiable facts to a generative engine:

Organization — legal name, business description, contact details, associated social profiles. This is often the first thing a generative model checks to establish who you actually are.
FAQPage — each tagged question/answer pair becomes an isolated, easily citable factual unit, instead of a fragment buried inside a long article.
Product — features, price, availability, aggregated customer reviews, all exposed in a format the model can directly compare against competing sources.
Person — for named experts identified on the site, this markup links their expertise to verifiable facts (education, publications, affiliation), which strengthens the credibility of any content they author.

Entity-Based SEO

Beyond technical markup, how you name and describe your brand across the entire site matters just as much. Entity-based SEO means treating your brand, products, and expertise as coherent entities named identically everywhere, rather than rephrased differently page after page for stylistic variety.

Concretely:

Consistently use the same brand name, spelling, and business description across the entire site and external profiles.
Explicitly link entities to one another - for example, a blog post mentioning a service should create a real link to that service’s page, not just a text mention.
Avoid vague marketing synonyms (“innovative solutions,” “operational excellence”) that provide no extractable information, in favor of concrete, quantified facts.

Building a Citable Data Feed for AI

A rapidly growing practice is centralizing a company’s verifiable facts - key statistics, dates, figures, certifications - into a single, up-to-date structured source, sometimes called an “LLM citation feed.” This can take the form of a dedicated “facts and figures” page, combined with consistent JSON-LD markup across the entire site, serving as the single reference any generative engine can consult and confidently cite rather than having to reconstruct information from scattered fragments.

Verifying Your Site Is Technically Extractable

None of these techniques work if the content itself isn’t accessible to the crawlers exploring it - whether that’s Googlebot or a crawler associated with a generative engine. A crawlability audit (robots.txt, sitemap, heading structure) remains the unavoidable technical prerequisite.

Run a free technical audit of your site →

Structuring Your Content for AI Citation

Rolling out consistent Schema markup across an entire site, while avoiding the pitfalls of incomplete or incorrect markup that can actually hurt the engine’s confidence, requires dedicated technical expertise. It’s one of the areas we build into our technical SEO services.

Get a free technical audit →

Published by the Runkexpert Engineering Team. Last updated July 15, 2026.