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Extract named entities (persons, organizations, dates, locations) from text and provide them in structured JSON-LD format.
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Basado en la clasificación ocupacional SOC
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| name | nlp_expert |
| description | Extract named entities (persons, organizations, dates, locations) from text and provide them in structured JSON-LD format. |
The NLP Expert skill uses Gemini 3 to perform advanced Named Entity Recognition (NER). It identifies key entities within dataset descriptions, transcripts, or web results and maps them to standard Schema.org types in JSON-LD.
This is critical for establishing provenance, identifying dataset creators, and mapping geographic and temporal coverage in the Croissant specification.
Analyzes text or a file and returns all detected entities as JSON-LD. Results are stored in ./data/nlp/.
Usage:
# Process raw text
python3 nlp_expert/scripts/extract_entities.py "Sergei Bodrov was born in Moscow in 1971."
# Process a file (e.g., a transcript)
python3 nlp_expert/scripts/extract_entities.py data/transcripts/6cWcZ2G53gE.txt
Example Output (JSON-LD):
{
"@context": "https://schema.org/",
"@type": "ItemList",
"itemListElement": [
{
"@type": "Person",
"name": "Sergei Bodrov"
},
{
"@type": "Place",
"name": "Moscow"
}
]
}