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AI Entity Visualiser

Use cases

Content entity analysis Semantic content optimisation Visualising topic coverage

Uses OpenAI GPT models with JSON response formatting to extract entities from text.

Segments content into token-based batches via tiktoken for longer documents.

Builds three-level hierarchy: spaCy-style labels → descriptive tags → entities with occurrence counts.

Renders interactive D3.js circle packing visualisation with Jinja2 HTML templating.

Streamlit App Requires API Key

Platform

Browser-based (no installation required)

Input

OpenAI API key

Text content to analyse

Model: gpt-4o-mini (default) or gpt-4o

Max tokens: 500-2000 (default: 1000)

Output

Interactive entity visualisation and CSV

Launch App View Source

Features

  • GPT-4o-mini or GPT-4o model selection
  • Tiktoken batch processing for long documents
  • Three-level hierarchy: labels → tags → entities
  • Regex context extraction (5-word window)
  • D3.js zoomable circle packing via Jinja2
  • Wikipedia URL linking per entity
  • stqdm progress tracking for batch processing

How to use

  1. 1 Enter your OpenAI API key
  2. 2 Select model (gpt-4o-mini recommended)
  3. 3 Configure max tokens (500-2000)
  4. 4 Paste text content
  5. 5 Click Process – tiktoken batches long text automatically
  6. 6 Explore interactive D3 circle packing hierarchy

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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