> ## Documentation Index
> Fetch the complete documentation index at: https://www.edenai.co/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Named Entity Recognition

> Named Entity Recognition (also called entity identification or extraction) is an information extraction technique that automatically identifies named entities in a text and classifies them into

export const TechArticleSchema = ({title, description, path, articleSection, about, proficiencyLevel = "Beginner", dependencies, keywords = [], datePublished, dateModified, image, inLanguage = "en"}) => {
  const baseUrl = "https://www.edenai.co/docs";
  const canonicalUrl = `${baseUrl}/${path}`.replace(/\/+$/, "");
  const ogParams = new URLSearchParams({
    division: articleSection || "",
    title: title || "",
    description: description || ""
  });
  const resolvedImage = image || `https://edenai.mintlify.app/_mintlify/api/og?${ogParams.toString()}`;
  const data = {
    "@context": "https://schema.org",
    "@type": "TechArticle",
    "@id": `${canonicalUrl}#techarticle`,
    mainEntityOfPage: {
      "@type": "WebPage",
      "@id": canonicalUrl
    },
    headline: title,
    name: title,
    description: description,
    url: canonicalUrl,
    inLanguage: inLanguage,
    isPartOf: {
      "@type": "WebSite",
      name: "Eden AI Documentation",
      url: baseUrl
    },
    author: [{
      "@type": "Organization",
      name: "Eden AI",
      url: "https://www.edenai.co/"
    }],
    publisher: {
      "@type": "Organization",
      name: "Eden AI",
      url: "https://www.edenai.co/",
      logo: {
        "@type": "ImageObject",
        url: "https://www.edenai.co/assets/logo.png"
      }
    }
  };
  if (articleSection) data.articleSection = articleSection;
  if (about) data.about = {
    "@type": "Thing",
    name: about
  };
  if (proficiencyLevel) data.proficiencyLevel = proficiencyLevel;
  if (dependencies) data.dependencies = dependencies;
  if (keywords && keywords.length) data.keywords = keywords;
  if (datePublished) data.datePublished = datePublished;
  if (dateModified) data.dateModified = dateModified;
  data.image = Array.isArray(resolvedImage) ? resolvedImage : [resolvedImage];
  const json = JSON.stringify(data);
  const schemaId = `techarticle-${canonicalUrl}`;
  React.useEffect(() => {
    if (typeof document === "undefined") return;
    document.querySelectorAll(`script[data-schema-id="${schemaId}"]`).forEach(n => n.remove());
    const script = document.createElement("script");
    script.type = "application/ld+json";
    script.dataset.schemaId = schemaId;
    script.textContent = json;
    document.head.appendChild(script);
    return () => script.remove();
  }, [json, schemaId]);
  return null;
};

<TechArticleSchema title={`Named Entity Recognition`} description={`Named Entity Recognition (also called entity identification or extraction) is an information extraction technique that automatically identifies named entities in a text and classifies them into`} path="v3/expert-models/features/text/named-entity-recognition" articleSection="Text Features" about={`NLP API`} proficiencyLevel="Intermediate" keywords={[`Eden AI`, `AI API`, `text analysis`, `NLP`]} datePublished="2026-05-06T00:00:00Z" dateModified="2026-05-07T00:00:00Z" />

## Endpoint

`POST /v3/universal-ai` (sync)

Model string pattern: `text/named_entity_recognition/{provider}[/{model}]`

## Input

| Field    | Type   | Required | Description                        |
| -------- | ------ | -------- | ---------------------------------- |
| text     | string | Yes      | Text to analyze for named entities |
| language | string | No       | ISO 639-1 language code            |

## Output

| Field          | Type           | Required | Description |
| -------------- | -------------- | -------- | ----------- |
| **items**      | array\[object] | No       |             |
|     entity     | string         | Yes      |             |
|     category   | string         | Yes      |             |
|     importance | float          | Yes      |             |

## Available Providers

| Provider        | Model String                                  | Price                     |
| --------------- | --------------------------------------------- | ------------------------- |
| amazon          | `text/named_entity_recognition/amazon`        | \$1 per 1,000,000 chars   |
| microsoft       | `text/named_entity_recognition/microsoft`     | \$1 per 1,000,000 chars   |
| openai          | `text/named_entity_recognition/openai`        | \$40 per 1,000,000 chars  |
| openai (gpt-4o) | `text/named_entity_recognition/openai/gpt-4o` | \$10 per 1,000,000 tokens |
| tenstorrent     | `text/named_entity_recognition/tenstorrent`   | \$1 per 1,000,000 chars   |

## Quick Start

<CodeGroup>
  ```python Python theme={null}
  import requests

  url = "https://api.edenai.run/v3/universal-ai"
  headers = {
      "Authorization": "Bearer YOUR_API_KEY",
      "Content-Type": "application/json"
  }

  payload = {
      "model": "text/named_entity_recognition/amazon",
      "input": {
          "text": "The quick brown fox jumps over the lazy dog.",
          "language": "en"
      }
  }

  response = requests.post(url, headers=headers, json=payload)
  print(response.json())
  ```

  ```bash cURL theme={null}
  curl -X POST https://api.edenai.run/v3/universal-ai \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "text/named_entity_recognition/amazon",
      "input": {"text": "The quick brown fox jumps over the lazy dog.", "language": "en"}
    }'
  ```
</CodeGroup>