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An entity extractor finds the named things in a piece of text: people, organisations, places, brands, products, dates and the key concepts it keeps returning to. The free Entity Extractor lists each entity with its type, number of mentions and a salience score, and in bulk mode it compares up to 20 texts or URLs to show which entities your competitors cover that your page does not. It is built for semantic SEO, topical gap analysis and writing about and mentions schema.
Last updated: 6 November 2026. Written by Saad Raza.
Key takeaways
- Search engines and AI systems understand pages through entities: specific, identifiable things such as Karachi, the American Dental Association or WordPress.
- Salience is how central an entity is to a text. The main subject should be mentioned early and often.
- The entity gap view shows the names and concepts that two or more competing pages use and yours does not.
- Entity types are estimated from capital letters, titles, name endings and place lists, so check the list before you use it in schema.
What is an entity in SEO?
An entity is a single, well-defined thing that can be identified, such as a person, place, organisation, product or idea. “Lahore” is an entity. “Pakistan Medical and Dental Council” is an entity. “Teeth whitening” is a concept that search engines also treat as a topic with its own attributes. Google’s Knowledge Graph stores billions of these and the relationships between them, and its systems connect the words on a page to them.
Writing that names the right entities is easier for search engines and AI assistants to place in context. A page about teeth whitening in Karachi that names neighbourhoods, a regulator, whitening systems and the active ingredient tells a much clearer story than one that repeats “best teeth whitening” ten times. That is the core idea behind entity-based SEO and semantic SEO.
What the Entity Extractor shows
| Output | What it tells you |
|---|---|
| Named entities by salience | Each person, organisation, place, product or other name, with mentions and a 0 to 100 salience score |
| In opening | Whether the entity appears in the first part of the text, where the main subject belongs |
| Key concepts | Repeated lowercase terms and phrases, such as “teeth whitening” or “peroxide gel” |
| Entity gaps (bulk) | Entities and concepts other texts use that the selected text does not |
| Coverage table (bulk) | Mentions of every entity in every text, side by side |
| Starter schema | A WebPage about and mentions JSON-LD block built from the top entities |
Salience combines three signals: how often an entity is mentioned compared with the most mentioned one, whether it appears in the opening, and how early it first appears. It is a guide to focus, not a measure of quality.
How to use the Entity Extractor
- Paste an article, page copy or HTML into the text box, or upload text, Markdown or HTML files.
- To compare pages, enter up to 20 URLs, one per line, such as your page and the pages that rank above it. You can also paste several texts separated by a line containing only ===, starting each with a # title line to name it.
- Press Extract entities.
- For one text, read the entity table from the top. Check that your main subject has the highest salience and appears in the opening.
- For several texts, choose your page under “Find gaps for” to see the entities and concepts the others use that you do not.
- Download the CSV, and review the starter schema before adding it to your page.
Example output
The Load example button compares two short texts. The first, about teeth whitening in Karachi, yields 12 named entities, including Karachi, Clifton and Gulshan-e-Iqbal as places, Ayesha Khan as a person because she is introduced as Dr, Smile Dental Clinic, the American Dental Association and the Pakistan Medical and Dental Council as organisations, and Philips Zoom and Opalescence as names. The second, about whitening costs in Lahore, names only Lahore, DHA and Gulberg. Choosing the Lahore text under “Find gaps for” lists the regulator, the whitening systems and the professional body it never mentions, a quick way to see where it is thin.
How to use entity gaps without keyword stuffing
An entity gap is a prompt to ask a question, not an instruction to add a word. If three competing pages all mention the regulator that licenses dentists and yours does not, ask whether a reader would benefit from knowing it. Usually they would, and the honest fix is a sentence or a short section that explains it. If a competitor mentions a brand you do not sell, ignore it. Good entity coverage comes from answering the reader’s real questions completely, which is also how topical authority is built across a site.
- Compare your page with the top three to five ranking pages for the main keyword.
- Sort the gaps into must cover, nice to have and not relevant.
- Add must-cover entities where they answer a real question, with a fact or a source.
- Link to your own pages about those entities where you have them, using descriptive internal links.
- Run the comparison again and check that your main subject still has the highest salience.
Entities, schema and AI search
Structured data lets you state your page’s entities explicitly. The about property names the main subjects and the mentions property names other entities discussed. Organization schema with sameAs links connects your business to its profiles elsewhere, which our guide to Organization schema explains. Validate any JSON-LD you write with the JSON-LD Validator before you publish it.
AI assistants rely on entities too. A passage that names specific places, organisations and products is easier to match to a question and easier to trust. That is why the AI Citation Readiness Scorer gives points for named entities, and why entity salience and Knowledge Graph optimisation matter for brands that want to be recognised in AI answers.
Limits to know about
The extractor uses rules, not a trained language model. It finds names from capital letters, titles such as Dr and Mr, organisation endings such as Ltd, Clinic and University, a list of countries and major cities including those in Pakistan and the Gulf, and a list of well-known brands. It can mislabel a place as a person or miss a name written in lower case, and it works best on English text. Words that only ever appear at the start of a sentence are left out to avoid counting ordinary words as names. Always review the list before you rely on it, especially before adding entities to schema.
Questions about the Entity Extractor
What does an entity extractor do?
An entity extractor reads text and lists the named things in it, such as people, organisations, places, brands and products, along with key concepts. It shows how often each appears and how central it is to the text.
What is entity salience?
Entity salience is a measure of how central an entity is to a text. An entity mentioned often and early, especially in the opening, has high salience. The main subject of a page should have the highest salience.
How do I find entities my competitors use?
Enter your page and up to 19 competitor URLs, then choose your page under “Find gaps for”. The tool lists entities and concepts that the other pages mention and yours does not.
Is this the same as Google’s Natural Language API?
No. Google’s API uses a trained model and links entities to its Knowledge Graph. This tool uses transparent rules and runs free in your browser. It is good for comparing pages quickly, but types are best guesses.
Can I use the output as schema?
The tool gives a starter WebPage block with about and mentions properties. Review it, correct any types, add sameAs links where you know them and validate the result before you add it to a page.
Does the tool store my text?
No. Pasted text and files are processed in your browser. URLs are fetched once by our server and not stored.
If you would like a full semantic SEO and entity review of your key pages, my on-page optimisation service covers content and entity coverage, and you can ask for a free audit through the contact page. You can also browse the other free AI search tools.
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