AI Entity Extractor
Detect names, places, and entities in any text
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Who is named in this document, and what are they? That question sounds trivial until you have forty pages of contract, meeting minutes or news coverage and someone needs a list of every company, person, place, date and amount mentioned in it. Reading for names is a different skill from reading for meaning, and most people are bad at doing both at once.
AI Entity Extractor does the naming pass. You supply the text, it returns the named things inside it, sorted into the categories they belong to.
Short answer: AI Entity Extractor identifies the named items inside a block of text, such as people, organisations, locations, dates and amounts, and returns them grouped and labelled. It runs free in the browser, takes pasted text of any length, and lets you set how strictly it decides what counts as an entity.
What is AI Entity Extractor?
An extraction tool with one narrow job. Rather than summarising a document or answering questions about it, AI Entity Extractor lists the specific things it names.
The interface is a paste box carrying the prompt Paste the text for the entity extractor, an engine selector below it, an accordion of ten settings, and a generate button. Nothing installs and no sign in stands between you and a result.
Names, not summaries
The output is a list of the things the document actually names, not a paraphrase of what it says.
Strictness that matters
Light keeps borderline mentions, Aggressive keeps only unambiguous entities. This one setting changes the list size dramatically.
Grouped output
Table and Structured styles keep each category separate instead of returning one long undifferentiated list.
Annotated mode
Turn Show Changes on with the Annotated style and each entity arrives with a note on why it was classed that way.
Why Use AI Entity Extractor?
The first reason is completeness. Human readers reliably miss the fifth mention of a company that was already noted twice, and they reliably miss names that appear only in a footnote. A machine pass does not get bored on page six.
The second is that the categories stay stable. When one person tags a document, "the London office" might be a location and might be an organisation. Set Strictness and Operation Focus once in AI Entity Extractor and every document in the set gets the same ruling.
Strengths
- Reads a long document evenly from start to finish
- Consistent category decisions across many files
- Output shape switches without re running the thinking
- Annotated mode makes its reasoning inspectable
Limits
- Nicknames and abbreviations may need Custom Instructions
- It cannot resolve two people who share a surname
- Fictional and real names look alike to it
- Very short text gives it too little context to classify well
Note Entities are read out of the text you paste. The tool does not look anything up, so it will not tell you whether a named company still exists.
How Does AI Entity Extractor Work?
Paste, choose, set, generate. In more detail:
| Stage | What happens |
|---|---|
| Prompt area | Your text arrives, formatting and all. Length is not a problem. |
| Model selector | Engines are grouped by provider. Running the same text past two of them is a quick reliability check. |
| Advanced Options | Ten controls decide the operation, the strictness and the shape of the answer. |
| Result card | The entity list appears with a live word count, plus copy, listen, reuse, download and open actions. |
Below the card an export row offers DOC, TXT and HTML, and an activity history panel keeps the last several runs so you can compare a Light pass against an Aggressive one without retyping anything.
Step-by-Step Guide
- Paste the document into the prompt area.
- Pick an engine.
- Open Advanced Options and set Operation Focus to Extract.
- Choose Strictness. Standard is the sensible starting point for prose.
- Set Output Style to Table if you want categories in columns.
- Add any category rules to Custom Instructions, for example "treat product names as organisations".
- Generate.
- Scan for anything obviously missing, adjust Strictness one step, and run again.
Caution Aggressive strictness drops entities that appear only once. On a legal document that is often exactly the entity that mattered.
Advanced Options Guide
Ten controls, and for entity work three of them carry most of the weight.
| Field | How it shapes the output |
|---|---|
| Operation Focus | Clean Up, Format, Normalize, Extract, Improve, Restructure, Standardize or Analyze. Extract is the setting for this job. |
| Output Style | Clean Text, Formatted, Structured, Bullet Points, Table or Annotated. |
| Strictness | Light, Standard, Strict or Aggressive. The single biggest influence on how long the list is. |
| Reading Level | Simple, General, Professional or Academic, applied to any explanatory text. |
| Preserve Meaning | Toggle. Keeps names spelled as the source spelled them. |
| Keep Formatting | Toggle. Holds on to the layout of the pasted text. |
| Fix Grammar | Toggle. Tidies the surrounding wording. Best left off for name work. |
| Show Changes | Toggle. Marks what was interpreted rather than lifted directly. |
| Intensity | Slider from 1 to 100, governing how far the operation is pushed. |
| Custom Instructions | Up to 1000 characters. Category rules belong here. |
Example Inputs
The tool is happy with ordinary prose. A short news style paragraph works well as a test:
Tip: Paste something you already know the answer to on your first run. If the extractor misses a name you can see with your own eyes, adjust Strictness before trusting it on a document you have not read.
Other inputs people bring to it include meeting minutes with attendee lists, supplier contracts full of company names and dates, research abstracts naming institutions, and support threads where the customer names three products in passing.
Example Outputs
Run a paragraph about a supplier agreement with Output Style on Table and Strictness on Standard, and the result groups itself:
| Entity | Type | Mentions |
|---|---|---|
| Halverson Freight | Organisation | 4 |
| Rotterdam | Location | 2 |
| 1 September | Date | 1 |
| 12,400 euro | Amount | 1 |
Change Output Style to Bullet Points and the same entities arrive as a flat list, useful when you only need names and not counts. Change it to Annotated and each line gains a short justification, which is what you want when someone will question the classification later.
Tips & Common Mistakes
- ✅ Start on Standard strictness and move one step at a time
- ✅ Tell the tool your category rules in Custom Instructions before the first run
- ✅ Use Annotated when the list will be reviewed by someone else
- ✅ Keep Preserve Meaning on so names are not tidied into different spellings
- ✅ Compare a second engine when the document is important
Three mistakes come up repeatedly. Pasting a title and expecting entities, when there is no surrounding text to classify against. Leaving Fix Grammar on, which occasionally corrects an unusual surname. And assuming the counts are exact when a name appears in both full and abbreviated form, which the tool may or may not merge depending on how strict you set it.
Pro tip Run the same document twice, once on Light and once on Strict. The entities that appear in both lists are the ones you can rely on without checking.
AIToolsay is where AI Entity Extractor sits, one of a broad collection of AI tools that each handle a single job end to end. There is no charge for any of them and no account to create, which means the gap between finding a tool and getting an answer out of it is about twenty seconds. Every page carries the same engine selector, so a second opinion is always available when a result looks off. If the text you are working through needs tidying before extraction, the AI Text Cleaner is the sensible first pass, and sales teams turning conversations into records tend to reach for the AI CRM Notes Generator afterwards. Everything else is listed on AIToolsay.
Frequently Asked Questions
Does AI Entity Extractor cost anything?
No. It is free to use and no account is needed to generate a result.
Which entity types does it recognise?
The usual ones found in prose: people, organisations, places, dates, amounts and similar named items. You can narrow or widen that through Custom Instructions.
Can I make it return only companies?
Yes. Say so in Custom Instructions. A single line such as "return organisations only" is enough.
Does it merge different spellings of the same name?
Sometimes, depending on Strictness. If exact counts matter, check a sample rather than assuming.
How long can the input be?
Long passages are fine. Splitting a very long document by chapter usually produces cleaner grouping than one enormous paste.
Can I export the list?
Yes, through the export row under the result card, which offers DOC, TXT and HTML.
Naming what a document names is a small task with a long tail. It is fine on one page and miserable across fifty. AI Entity Extractor makes the fiftieth page as accurate as the first, which is the only sort of consistency that actually shows up in the finished work.
Thanks for reading this far. If the tool proves useful, tell the AIToolsay community what you pointed it at, follow AIToolsay on social media, switch on push notifications so new releases find you, and subscribe to the newsletter for the round up.
Let AI Speak.
Sabir
Founder of AIToolsay and a passionate AI enthusiast dedicated to building practical, user-friendly AI tools that simplify everyday tasks.
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