Demonstrates infospace composition: the Wealth of Nations infospace is
used as a discipline, applying Smith's economic framework as a lens to
analyse modern supply chain management concepts.
New example: examples/supply-chain-vsm/
- infospace.yaml binding WoN as discipline (../infospace-with-history)
- 3 source documents: coordination mechanisms, capital & inventory,
market structure (~400 words each, original content)
- supply-chain-entity-schema-v1.0.md with WoN Concept required section
- won-mapping-schema-v1.0.md with Conceptual Continuity rating
- artifacts/won-reference/core-entities.md — 12 curated WoN entities
for injection as discipline context
- 8 hand-crafted entity files demonstrating LLM output format
- 3 mapping files with full rationale and VSM inheritance chains
- Viable: YES (5/5 thresholds)
Key mappings demonstrated:
Demand Signal → Effectual Demand (Strong, S2)
Vendor-Managed Inventory → Division of Labour (Strong, S1/S2)
Just-in-Time Inventory → Circulating Capital (Strong, S1/S3)
Bullwhip Effect → Natural Price (Moderate, S2)
Platform Intermediary → Merchant Capital (Strong, S2/S4)
Monopsony Power → Combination of Masters (Strong, S3*)
Platform fix: entity_parser.py now recognises ## Supply Chain Domain
as a domain alias for ## Economic Domain, enabling composed infospaces
to use their own domain section name.
Tutorial §13 rewritten with real commands, real output, and the full
mapping table from the demo.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add `.*-raw\.md$` to `_DEFAULT_EXCLUDE_PATTERNS` in entity_parser.py to
prevent per-chapter raw LLM output files from being parsed as entities.
This eliminates 33 malformed domain values where delimiter text was
bleeding into the Economic Domain field.
- Lower coverage_ratio threshold from 0.50 → 0.40 in infospace.yaml to
reflect realistic multi-book corpus expectations (documented rationale
in METRICS-METHODOLOGY.md).
Post-fix metrics: 988 entities, 0 malformed, coverage_ratio=0.619 (pass).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Extract section-tree algorithm from SchemaGenerator into standalone
core/section_tree.py and build markitect/infospace/ package with
EntityMeta dataclass and parse_entity_file/parse_entity_directory.
Foundation for schema compliance, coverage, and granularity metrics.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
