# V5 Extraction - Quick Reference

## Status: ✅ 75% PRECISION ACHIEVED

### Architecture
```
Conversation Text → Subagent NER → V5 Validation → Clean Institutions (75% precision)
```

### What Works
- ✅ **Subagent NER**: Clean, accurate names (no mangling)
- ✅ **V5 Validation**: 3 filters (country, organization, proper name)
- ✅ **75% precision**: 3/4 correct (up from V4's 50%)

### What Doesn't Work
- ❌ **Pattern-based extraction**: 0% precision (names mangled)

### Commands

**Run V5 demonstration:**
```bash
bash /Users/kempersc/apps/glam/scripts/demo_v5_success.sh
```

**Test subagent + V5 integration:**
```bash
python /Users/kempersc/apps/glam/scripts/test_subagent_v5_integration.py
```

### Subagent NER Prompt Template

```
Extract ALL heritage institutions from the following text.

Return JSON array with:
{
  "name": "Full institution name",
  "institution_type": "MUSEUM | ARCHIVE | LIBRARY | GALLERY",
  "city": "City name",
  "country": "2-letter ISO code",
  "isil_code": "ISIL code if mentioned",
  "confidence": 0.0-1.0
}

Rules:
1. Preserve full names (e.g., "Van Abbemuseum", not "Abbemuseum")
2. Classify by primary function
3. Determine country from city names or context
4. Exclude: organizations, networks, generic descriptors
```

### Next Steps for Production
1. Implement `extract_from_text_subagent()` in `InstitutionExtractor`
2. Update batch extraction scripts
3. Process 139 conversation files

### Files
- **Documentation**: `output/V5_VALIDATION_SUMMARY.md`
- **Session Summary**: `SESSION_SUMMARY_V5.md`
- **Test Scripts**: `scripts/test_subagent_v5_integration.py`
- **Demo**: `scripts/demo_v5_success.sh`

---

**Result:** V5 achieves 75% precision via subagent NER + validation filters