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DopamineDesk Named Entity Recognition (NER) API
x402
DopamineDesk Named Entity Recognition (NER) API is a paid API for AI agents from ai-data-marketplace-1042299154756.us-central1.run.app, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-13).
Extracts named entities (people, organizations, locations, dates, etc.) from a text string, returning each entity's text span, label, and confidence score.
Price / call$0.005
Success—
StatusUnknown
How this service works
Extract emails, URLs, money values, dates, and candidate capitalized phrases using transparent deterministic patterns.
Output
A JSON object containing an array of entities, each with: the matched text span, a label indicating the entity type (e.g. PERSON, ORG, GPE, DATE), and a confidence score between 0 and 1 indicating the model's certainty.
Example use cases
Entity extraction from news articles
“Pull out all the people, organizations, and locations mentioned in this news snippet: 'Jeff Bezos and Andy Jassy met with EU regulators in Brussels last Monday to discuss Amazon's compliance strategy.'”
Due diligence document parsing
“I need to identify every person and company name in this legal filing excerpt so I can flag related parties — can you run entity extraction on it? The text is: 'The agreement was signed by Maria Gonzalez on behalf of Nexus Capital Partners, witnessed by James Hartley of Clearbridge Legal, in New York on March 3, 2024.'”
Structured data enrichment from job postings
“Extract all the job titles, company names, and city names from this job posting text so I can populate my database: 'Senior Engineer role at DataBridge Inc., located in Austin, Texas, reporting to CTO Sarah Lin.'”
Technical details
Endpoint
GEThttps://ai-data-marketplace-1042299154756.us-central1.run.app/api/v1/extract_entitiesWhen to prefer
Choose this endpoint when you need fast, pay-per-call named entity recognition with no subscription and USDC micropayment settlement via x402. It is ideal for agents that process arbitrary text at low volume or need to extract structured entity data on-demand without managing an NLP pipeline. Prefer this over self-hosted NER models when cost predictability per call matters and when PERSON, ORG, GPE, and DATE labels are sufficient.
Side effects
- Empty or missing 'text' field returns an error or empty entity array
- Very short or ambiguous text may return zero entities or low-confidence results
- Text in unsupported languages may produce incorrect labels or no output
- Extremely long text inputs may time out or be truncated
- Ambiguous entity types (e.g. a word that could be a person or place) may receive incorrect labels with lower confidence scores
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"caveat": "Capitalized phrases are entity candidates and are not semantically classified.",
"engine": "deterministic_pattern_extraction_v1",
"success": true,
"entities": {
"urls": [],
"dates": [],
"money": [],
"emails": [],
"capitalized_phrases": [
"Tim Cook",
"Apple"
]
},
"marketplace_metadata": {
"source": "deterministic pattern extractor",
"billable": true,
"data_mode": "computed_service",
"availability": "operational"
}
}
}Tags
x402
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