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nlp_expert

Extract named entities from text into standardized JSON-LD using Gemini 3 NLP.

8|1|Updated Mar 14, 2026
Search Tags:#nlp#gemini#metadata extraction#json-ld#named entity recognition#semantic data
One-click install
npx skills add https://github.com/codata/croissant-toolkit --skill nlp-expert-codata
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nlp_expert
Source: https://github.com/codata/croissant-toolkit/tree/main/.gemini/skills/nlp_expert
Command: npx skills add https://github.com/codata/croissant-toolkit --skill nlp-expert-codata

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables advanced extraction of named entities from unstructured text, aiding in data provenance and metadata enrichment.

Core Features & Use Cases

  • Entity Recognition: Analyzes text or files to detect persons, organizations, locations, dates, AI models, monetary amounts, and quantities.
  • Structured Output: Presents recognized entities in standardized JSON-LD format compatible with Schema.org.
  • Use Case: For instance, extracting all relevant entities from a dataset description to improve dataset discoverability and linking in semantic platforms.

Quick Start

Use the nlp_expert skill to process the text "Sergei Bodrov was born in Moscow in 1971" and retrieve structured entity data.

Frequently Asked Questions about nlp_expert

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract named entities from unstructured text into JSON-LD?▼

To extract named entities into JSON-LD, process your text or files through an NLP API to identify persons, organizations, and locations, then output the results as standardized semantic annotations. This approach structures unstructured data for data catalogs.

What is the best way to automate metadata extraction for dataset discoverability?▼

Automating metadata extraction is best achieved by using NLP to analyze dataset descriptions, recognize entities like dates and AI models, and generate standardized JSON-LD. This enriches data provenance and links datasets in semantic platforms.

How does named entity recognition handle multilingual content?▼

Named entity recognition handles multilingual content by processing the text through advanced NLP APIs that detect entities across different languages. It then structures these recognized entities into a standardized JSON-LD format.

Can I use NLP to extract monetary amounts and quantities from files?▼

Yes, you can use NLP to extract monetary amounts and quantities from files. The API analyzes the textual data to detect these specific entities alongside persons and locations, structuring them into JSON-LD format.

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