Entity Types

The following entity types are detected by the PII and NER models.

PII Entities

Name	Description	Example
PERSON_NAME	People's names (first, last, full)	John Doe, Sarah J. Parker
NATIONAL_ID	National IDs: SSN, driver's license, passport numbers	123-45-6789
EMAIL_ADDRESS	Email addresses	john@example.com
PHONE_NUMBER	Phone numbers (landline, mobile, international)	+1-555-1234
ADDRESS	Street addresses, cities, states, ZIP codes	123 Main St, New York, NY 10001
DATE	Dates (absolute and relative)	2024-01-15
CREDIT_CARD	Credit/debit card numbers (PAN)	4111-1111-1111-1111
IBAN	International Bank Account Numbers	GB82 WEST 1234 5698 7654 32
VAT_NUMBER	Tax registration IDs (VAT, GST, EIN, ITIN)	GB123456789
USERNAME	Usernames and account identifiers	johndoe
ZIP_CODE	US ZIP codes (5 or 9 digit)	10001
LICENSE_KEY	Software license keys	XXXXX-XXXXX-XXXXX-XXXXX
API_KEY	API keys for SaaS services	sk-abc..., qa_live_xxx

NER Entities

Name	Description	Example
ORGANIZATION	Companies, agencies, institutions	Acme Corp, Stanford University
LOCATION	Geographic locations, landmarks	Eiffel Tower, Grand Canyon
GPE	Geo-political entities (countries, cities, states)	France, California, London

How Detection Works

The PII recognizer (iiiorg/piiranha-v1-detect-personal-information) and the NER recognizer (elastic/distilbert-base-uncased-finetuned-conll03-english) run in parallel. Results are merged, deduplicated, and sorted by position in the original text.

Specifying Entities

By default, all entity types are detected. Use the entities parameter with a comma-separated string of entity names to filter:

{
  "text": "My name is John Doe and my SSN is 123-45-6789.",
  "entities": "PERSON_NAME,NATIONAL_ID"
}

List available entities at runtime:

curl https://demo.questa-ai.online/anonymize/entities

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Next: Custom Entities