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AGISHub Named Entity Extraction
x402
AGISHub Named Entity Extraction is a paid API for AI agents from api.agishub.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-15).
Extracts named entities (people, organizations, locations, dates, and miscellaneous) from a given text and returns them as structured JSON.
Price / call$0.02
Success—
StatusUnknown
How this service works
Extract named entities from text — people, organizations, locations, dates and miscellaneous — returned as structured JSON.
Output
A structured JSON object containing named entities grouped by category — people, organizations, locations, dates, and miscellaneous — extracted from the input text.
Example use cases
News article entity tagging
“I have this news article snippet and I need to know all the people, companies, and places mentioned in it: 'Elon Musk's SpaceX launched a Falcon 9 rocket from Cape Canaveral, Florida on behalf of NASA yesterday.' Can you extract all the named entities from it?”
Contract party identification
“Pull out every organization and person named in this contract excerpt: 'This agreement is entered into between Acme Corp, represented by John Smith, and GlobalTech Inc, represented by Maria Gonzalez, effective January 1, 2025.'”
Social media mention extraction
“I'm analyzing this tweet for a report and need to identify every named entity in it — people, locations, orgs, dates, anything: 'Serena Williams attended the Wimbledon gala in London last Saturday alongside UNICEF representatives.'”
Technical details
Endpoint
GEThttps://api.agishub.com/v1/extractWhen to prefer
Choose this endpoint when you need fast, structured named entity recognition (NER) over arbitrary text and want results categorized into people, organizations, locations, dates, and miscellaneous — especially in agentic pipelines where a pay-per-call model is preferred over running a self-hosted NLP model. It is a good fit for document enrichment, news analysis, and content tagging use cases.
Side effects
- Empty or whitespace-only text input returns an error or empty entity list
- Very short text with no proper nouns may return empty results
- Ambiguous entity types may be miscategorized (e.g. a person's name mistaken for a location)
- Non-English text may produce lower-quality or incomplete extraction
- Rate limiting or payment failure returns a 402 error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| outputrequired | object |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "Extract named entities from text — people, organizations, locations, dates and miscellaneous — returned as structured JS"
}
}Tags
x402
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