Entity Extractor

Paste your content and see the entities it actually signals to LLM retrieval systems — named entities, domain terms, and acronyms, ranked by salience. Export as JSON-LD or CSV.

Live v1.0 · Updated April 2026 Free · No login
Extractor Idle
Paste page copy or a draft. Analysis runs locally — nothing is fetched or sent to a server, so URL fetching is intentionally not supported.
Runs locally · nothing sent to a server

How it works

LLM-based search does not rank keywords — it retrieves and reasons over entities: the people, products, organizations, technologies, and concepts a page is genuinely about. This tool reads your pasted content and surfaces the entities it detects, so you can see your page the way a retrieval system roughly does.

It combines three passes: a curated dictionary of SEO and AI-search terms, proper-noun detection for named entities, and acronym detection. Each entity gets a salience score that weights how often it appears and how early, then everything is ranked.

Use it to check that a page about a topic actually mentions the supporting entities it should — and export the result as a mentions JSON-LD block or a CSV. It runs entirely in your browser.

Frequently asked

No. It is a fast, deterministic, rule-based extractor — dictionary matching plus proper-noun and acronym detection — running in your browser. That keeps it private and instant, but it will not match a full NLP pipeline on nuance. Treat it as a coverage check, not ground truth.
Because a browser cannot fetch arbitrary third-party URLs without being blocked by cross-origin rules, and fetching would mean sending data to a server — which breaks the privacy model every tool here follows. Pasting your own copy keeps it fully local.
It is a 0 to 100 weighting of how prominent an entity is in your text, based on how often it appears and how early it shows up. Higher salience means the entity is more central to what the page signals.
It gives you a WebPage object with a mentions array of the detected entities. Drop it into your structured data as a starting point, prune it to the entities that genuinely matter, then run it through the Schema Validator.