fix: update all Stage 2 references to Stage 3 CANONICAL type counts

Comprehensive update of type count references from Stage 2 (31 nouns + 40 verbs)
to Stage 3 CANONICAL (42 nouns + 127 verbs) across entire codebase.

Changes (23 files):
- Core architecture: Memory tracking comments, speedup calculations
- Tests: Type count assertions, enum index expectations, memory benchmarks
- CLI: User-visible type count output
- Augmentations: Type detection comments
- Documentation: Architecture docs, guides, performance docs
- Type embeddings: Regenerated for all 169 types (338KB)

Specific updates:
- 31 → 42 (noun count): 38 occurrences
- 40 → 127 (verb count): 24 occurrences
- 124 → 168 bytes (noun array size): 5 occurrences
- 160 → 508 bytes (verb array size): 5 occurrences
- 284 → 676 bytes (total type tracking): 12 occurrences
- Enum indices updated to match Stage 3 reordering

Type embeddings regenerated:
- 42 noun embeddings (64.5 KB)
- 127 verb embeddings (194.8 KB)
- Total: 338 KB (was 108.8 KB)

All constants, arrays, and tests now consistent with Stage 3 taxonomy.

Fixes #v5.5.1-type-count-migration

docs/FIND_SYSTEM.md

@@ -245,7 +245,7 @@ return results.slice(offset, offset + limit)
 ## Type-Aware NLP Features
 
 ### 1. Dynamic Field Discovery
-- **No Hardcoded Fields**: Only NounType/VerbType taxonomies are fixed (30+ noun, 40+ verb types)
+- **No Hardcoded Fields**: Only NounType/VerbType taxonomies are fixed (42 noun, 127 verb types)
 - **Real Data Learning**: Field affinity learned from actual indexed entities
 - **Semantic Matching**: "by" → "author" via embedding similarity (87% confidence)
 - **Type Context**: Documents have different fields than Persons or Organizations
@@ -287,7 +287,7 @@ return results.slice(offset, offset + limit)
 
 Where:
 - n = number of entities in database
-- t = number of types (70 total: 30 noun + 40 verb)  
+- t = number of types (169 total: 42 noun + 127 verb)  
 - f = number of fields for detected entity type (typically 5-15)
 
 ### Scalability