Analytics
Entity Extract
Pulls people, organisations, money amounts and dates (and optionally email addresses and phone numbers) out of text by pattern, one column per kind.
When to use it
Use it to mine tickets, emails or notes for the names, amounts and dates in them so you can filter, join or count them.
In a pipeline
File InputEntity ExtractExport CSV
Settings
| Setting | Type | Required | Default or hint |
|---|---|---|---|
| Text column | select | Yes | Select column… |
| People | checkbox | — | true |
| Organisations | checkbox | — | true |
| Money | checkbox | — | true |
| Dates | checkbox | — | true |
| Email addresses | checkbox | — | false |
| Phone numbers | checkbox | — | false |
Example
Mine a support ticket
- Text column=Ticket
| Ticket |
|---|
| Dr. Maria Lopez from Northwind Trading Ltd says invoice of $1,240.50 dated 12 March 2026 is unpaid. |
| Ticket | Persons | Organizations | Money | Dates |
|---|---|---|---|---|
| Dr. Maria Lopez from Northwind Trading Ltd says invoice of $1,240.50 dated 12 March 2026 is unpaid. | Dr. Maria Lopez | Northwind Trading Ltd | $1,240.50 | 12 March 2026 |
Worth knowing
- Pattern-based, not named-entity recognition: people are found after a title (Mr, Dr…) or a cue word (from, contact, Dear…); organisations end in Ltd, Inc, LLC, Bank, University…
- Several finds in one text are joined with “; ”.
- Money is a currency symbol or code with a number; dates are ISO, 12/03/2026 or written out (12 March 2026, March 12, 2026).