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Entity Extractor

Use cases

Optimising content for semantic search and Knowledge Graph Identifying missing entities compared to competitors Building comprehensive topic coverage Understanding what entities Google associates with a topic

Extracts named entities using spaCy NLP with selectable models (sm/md/lg for speed vs accuracy tradeoff).

Recognises 11 entity types: PERSON, ORG, GPE, LOC, PRODUCT, EVENT, WORK_OF_ART, LAW, LANGUAGE, NORP, and FAC.

Supports text input, HTML (auto-strips scripts/styles/nav), and batch CSV/Excel processing.

Streamlit App

Platform

Browser-based (no installation required)

Input

Text, HTML, or CSV/Excel for batch

Output

Entities by type with frequency counts (CSV)

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Features

  • spaCy NLP with 3 model sizes (en_core_web_sm/md/lg)
  • 11 entity types recognised (PERSON, ORG, GPE, LOC, PRODUCT, etc.)
  • HTML parsing with noise removal (scripts, styles, nav)
  • Batch processing via CSV/Excel upload
  • Text truncation limit (100k chars) for memory efficiency

How to use

  1. 1 Select spaCy model size based on speed vs accuracy needs
  2. 2 Paste text/HTML or upload CSV/Excel for batch processing
  3. 3 Select entity types to extract (filter by PERSON, ORG, etc.)
  4. 4 Run extraction and review entities grouped by type
  5. 5 Download full results CSV or aggregated entity counts

Want me to run this for you?

I offer this as a managed service. You get the insights without touching the tool.

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