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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 2026-09-14).
Extracts named entities (people, organizations, locations, dates, and miscellaneous) from a text string, returning 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 categorized named entities found in the input text, organized by entity type: persons, organizations, locations, dates, and miscellaneous entities.
Example use cases
Entity tagging for news articles
“Extract all named entities from this news excerpt so I can tag it: 'Apple CEO Tim Cook met with President Biden at the White House on Tuesday to discuss semiconductor policy.'”
CRM lead enrichment from email
“Here's an email body — pull out any people, companies, and locations mentioned so I can pre-fill a CRM record: 'Hi, I'm Sarah Chen from Acme Corp in Seattle. We'd love to schedule a call with your sales team.'”
Structured data from legal text
“I have this clause from a contract — identify any dates, organizations, and people named in it: 'This agreement is entered into on January 15, 2024, between Horizon Ventures LLC and Dr. James Caldwell, residing in New York.'”
Technical details
Endpoint
POSThttps://api.agishub.com/paid/extractWhen to prefer
Choose this endpoint when you need fast, structured NER from unstructured text without setting up your own NLP pipeline. It's ideal for enriching data records, tagging content, or preprocessing text before downstream analysis. Best for single-document or real-time extraction use cases at low cost ($0.02/call).
Side effects
- Empty or whitespace-only text input returns an error (minLength:1 enforced)
- Very short or ambiguous text may yield zero entities or incorrect classifications
- Text in unsupported or rare languages may produce low-quality or missing extractions
- Extremely long text inputs may time out or be truncated
- Ambiguous references (e.g., 'Apple' as fruit vs company) may be misclassified
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| outputrequired | object |
Tags
x402
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