Adds a closing remark (23 Feb 2026) summarising the final state of the
infospace: 988 entities, 985 evaluations, 823 L2 classifications, 15 L3
relations, viability 6/6 PASS.
New open tasks 20–23:
20. Complete L2 classification batch (165 entities blocked on credits)
21. Run classify-links for 58 Relation-type entities
22. Refresh stale metrics-report.md narrative
23. Smoke-test the graph command end-to-end
Also committed: history.py fix — write_metrics_file now preserves
non-float metric values (type_distribution dict) instead of crashing
on round().
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
INFRA-TASKS #5 — process_chapters.py now skips writing *-prompt.md files
when the corresponding output file already exists on disk. DB-only rebuilds
no longer dirty the working tree with unchanged prompt content.
INFRA-TASKS #8 — Added '## Quality Metrics' section to the entity and VSM
mapping schemas, defining the five evaluation dimensions (Definition Precision,
Source Grounding, Domain Placement, VSM Relevance, Explanatory Value) with
1–5 rubrics used by the evaluate-entity template.
Also updated INFRA-TASKS.md to reflect current resolution status for tasks
4–19 across S2 and S3.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Fix evaluate dimensions to match template file:
definition_precision, source_grounding, domain_placement,
vsm_relevance, explanatory_value (was domain_relevance,
discipline_alignment, conceptual_clarity)
- Add VSM background context to evaluation prompt so LLM can
score vsm_relevance without macro injection
- Fix model_name bug: was sending literal "default" to API (HTTP 400)
- Refactor run_entity_evaluation to write files incrementally via
callback rather than all at once after the batch — long runs are
now resumable if interrupted
- Add incremental skip in CLI: entities with existing eval files
are skipped automatically on re-run (acts as resume)
- Add eval-summary command: reads all eval files, shows per-dimension
means, optionally writes per_entity_mean to metrics.yaml
- Fix record_check_results to merge rather than overwrite metrics.yaml
so per_entity_mean survives subsequent check runs
- Add per_entity_mean viability threshold (min: 3.5) to infospace.yaml
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
Adds LAYERED-DEVELOPMENT.md documenting the concept for evolving a flat
entity collection into a structured systemic model through four layers:
L0 Source text → L1 Raw entities (current) → L2 Typed entities
→ L3 Relation graph → L4 Minimal systemic model
Covers: the element/relation/principle/institution type taxonomy,
VSM as a structural coordinate system, the type × VSM coverage matrix,
triplet extraction with a controlled predicate vocabulary, feedback loop
detection, and the distillation hypothesis for finding the generative
core of a corpus.
Extends TUTORIAL.md with sections 17–23:
17. Observing entity heterogeneity
18. The four-layer model overview
19. Layer 2 — classifying entities (schema, pipeline stage, metrics)
20. Layer 3 — extracting the relation graph (triplets, feedback loops)
21. Layer 4 — the minimal systemic model (core-model.md output)
22. Planned CLI commands for layers 2–4
23. Layers 2–4 as composed infospaces
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>
- coverage.py: rewrite module docstring to explain what the metric actually
computes (domain × chapter cross-tabulation, not VSM system coverage),
what it does not capture (entity connectivity → C3), and when the
threshold is appropriate
- CoverageReport: add domain_densities, density_std, cross_cutting_ratio
for distribution-level insight beyond the aggregate ratio
- check_coverage: compute per-domain density and cross-cutting ratio
- METRICS-METHODOLOGY.md: correct C2 section to match implementation,
document the distribution-based interpretation, add implementation status
table distinguishing what is wired vs planned
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
1021 entities extracted across all Books 1-5 of The Wealth of Nations.
Final metrics: coverage=0.4424, granularity=2.9533, redundancy=0.0059.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Chapters with many pre-existing entities were still truncating at 6000 tokens
because the LLM needs space to output the full list of candidates even when
most are skipped.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
map-to-vsm was consistently truncating at 6000 tokens; synthesize-analysis
sometimes truncated at 3000 for chapters with many entities.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- PipelineStage now supports max_tokens to override the 4096 default
- SourcePipeline records provider/model on each entity file as HTML comment
- output/processing-log.yaml tracks tokens, cost, duration, retries, errors
- _call_llm returns (content, metadata) for downstream traceability
- _http.py wraps JSON parse errors with body preview for debugging
- infospace.yaml stages: extract/map=6000 tokens, synthesize=3000 tokens
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
