Entity Detection & Mentions
As you write in the content editor, MythTapestry can detect references to entities in your text and suggest linking them to your knowledge graph. This feature is entirely optional — you can always link entities manually using the @ mention system instead.
How it works
Section titled “How it works”- You write content that mentions an entity (e.g. “Aldric rode to the Crimson Tower”)
- The system detects “Aldric” and “Crimson Tower” as potential entity references
- Suggestions appear as highlighted text with a subtle indicator
- You can confirm or dismiss each suggestion
Detection methods
Section titled “Detection methods”Two methods are available (configured in World Settings):
GLiNER (local, no AI)
Section titled “GLiNER (local, no AI)”GLiNER is a named-entity recognition model — it is not a large language model (LLM). It runs entirely on the MythTapestry server using pattern-matching and lightweight neural inference, with no external API calls.
- Fast — typically processes text in under a second
- No quota usage and no data sent to external services
- Good for common entity types (names, places, organisations)
- Uses fuzzy matching (via rapidfuzz) against your world’s entity names, aliases, and subtypes
- May miss domain-specific or context-dependent entities
AI-powered
Section titled “AI-powered”Uses your configured AI model via OpenRouter for more nuanced detection. This is better for unusual references and context-dependent names (e.g. recognising “The Crown” as a faction rather than a literal crown).
- More accurate for unusual or context-dependent references
- Uses your feature quota
- Better at understanding when common words are entity names
Detection modes
Section titled “Detection modes”| Mode | Trigger | Description |
|---|---|---|
| Manual | Click the “Detect Entities” button | You control when detection runs |
| Auto | On content save | Detection runs automatically when you save |
| Selection | Ctrl+Shift+E or right-click → Extract Entities | Runs on the selected text only |
The manual and auto modes are configured per world in World Settings. You can disable detection entirely if you prefer to manage mentions manually.
Selection-based extraction
Section titled “Selection-based extraction”Instead of scanning your whole document, you can extract entities from a specific passage:
- Select the text you want to analyse in the editor
- Press Ctrl+Shift+E, or right-click → Extract Entities
- Only the selected text is sent through the detection pipeline
- Results appear in the Entities tab as suggestions, ready to confirm or dismiss
This uses exactly the same GLiNER or AI pipeline as full-document detection — just scoped to your selection. It’s particularly useful for dense passages (introductions, infodumps, battle scenes) where you want to catch every reference without re-scanning work you’ve already reviewed.
Managing mentions
Section titled “Managing mentions”Single mention
Section titled “Single mention”When one entity is detected in a text selection, you see an inline confirm/dismiss action right at the mention location.
Multiple mentions
Section titled “Multiple mentions”When several entities are detected, they appear in a collapsible panel with:
- Bulk confirm — accept all suggestions at once
- Bulk dismiss — reject all suggestions
- Individual confirm/dismiss per mention
Mention states
Section titled “Mention states”| State | Appearance | Behaviour |
|---|---|---|
| Confirmed | Solid highlight, clickable link | Persists across saves, visible in knowledge graph |
| Suggested | Subtle highlight, action buttons | Survives saves but doesn’t create graph links |
| Dismissed | Removed | Won’t be suggested again for this text |
New entity suggestions
Section titled “New entity suggestions”When enabled (suggest_new_entities in World Settings), the detection system can flag text that looks like it refers to an entity that doesn’t exist yet in your world. These appear with a special indicator so you can decide whether to create the entity.
Confidence threshold
Section titled “Confidence threshold”The confidence threshold (default 0.7) controls how certain the detection must be before suggesting a mention. Lower values produce more suggestions but may include false positives. Higher values are more conservative.