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x402 AI Tools – Named Entity Recognition
x402
x402 AI Tools – Named Entity Recognition is a paid API for AI agents from ai.kagurazaka.dev, paid per call via x402, $0.008/call, status unknown (last checked 2026-10-30).
Extracts named entities (persons, locations, organizations, dates, etc.) with character offsets and confidence scores from text using Amazon Comprehend, with optional language auto-detection.
Price / call$0.008
Success—
StatusUnknown
How this service works
Finds named entities (person, location, organization, commercial item, event, date, quantity, title, other) in a text with Amazon Comprehend, with their offsets and scores. Supports en, es, fr, de, it, pt, ar, hi, ja, ko, zh, zh-TW; the language is detected when not given.
Output
A JSON object containing an array of entity objects, each with: the matched text span, entity type (organization, person, location, date, event, commercial item, quantity, title, or other), begin and end character offsets, and a confidence score (0–1). Also includes the language code used and a boolean indicating whether language was auto-detected.
Example use cases
Extract entities from news article
“I have this news paragraph and I need to pull out every person, organization, location, and date mentioned along with where they appear in the text: 'The United Nations convened in Geneva last Tuesday, where Secretary-General António Guterres met with EU officials.' Can you identify all named entities with their offsets and confidence scores?”
Tag entities in Spanish customer feedback
“This customer review is in Spanish — can you extract all named entities from it, including any brands, locations, and people mentioned? The text is: 'El presidente de Samsung, Lee Jae-yong, visitó la planta de Madrid el lunes pasado.'”
Analyze entities in legal document excerpt
“I need to identify every organization, person, and date in this contract excerpt so I can build a knowledge graph. Here's the text: 'On January 10 2024, Apex Holdings LLC agreed with Dr. Richard Ito of Boston Biotech to license the patent.' Please return each entity with its type, position, and score.”
Technical details
Endpoint
POSThttps://ai.kagurazaka.dev/v1/entities?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need structured, span-level named entity annotations (with character offsets and type labels) from multilingual text, backed by Amazon Comprehend's production-grade NER models. Prefer it over generic LLM prompting for NER when you need reliable confidence scores, consistent entity type taxonomy, and exact character positions — especially for non-English languages (12 supported). It is cost-effective at $0.008 per call for texts up to 5000 bytes.
Side effects
- Text exceeds 5000 bytes — request rejected or truncated
- Unsupported language code provided — error response
- Empty or whitespace-only text — returns empty entities array
- Language detection fails for very short or ambiguous text — may default or error
- Network/service timeout — HTTP 5xx response
- Payment failure via x402 protocol — 402 Payment Required if USDC payment not completed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | the text, up to 5000 bytes of UTF-8 |
| language | string | language code (en, es, fr, de, it, pt, ar, hi, ja, ko, zh, zh-TW); detected when omitted |
Output
Schema · Response
{
"type": "json",
"example": {
"entities": [
{
"end": 22,
"text": "Acme",
"type": "organization",
"begin": 18,
"score": 0.98
},
{
"end": 39,
"text": "Tokyo",
"type": "location",
"begin": 34,
"score": 0.99
}
],
"language": "en",
"language_detected": true
}
}Tags
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