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>
The SQLite artifact database is a derived cache regenerable from
committed files — no LLM calls needed. Added tutorial section
explaining why it is excluded and how to rebuild it after a fresh clone.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add OpenAIAdapter for the OpenAI chat completions API (apikey-chatgpt.txt
or OPENAI_API_KEY). Set default model to arcee-ai/trinity-large-preview:free
for the infospace pipeline and increase max_tokens from 4096 to 8192.
Reprocess chapter 05 with Trinity Large (was Gemini: 1 truncated entity,
now 19 complete entities). Process chapters 06 (Aurora Alpha, 10 entities)
and 07 (Trinity Large, 15 entities including regenerated violent-policy.md).
Canonical set now at 85 unique entities.
Add entity archive policy: entities are never silently deleted. Retired
entities move to output/entities/archive/ with a dated reason header.
New CLI option: --archive-entity <slug> --reason "...". The --list
output shows the archive count alongside the canonical set.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add GeminiAdapter calling Google's Generative Language REST API
(default model: gemini-2.5-flash). Register "gemini" as third
provider in the factory and CLI. Add rate-limit retry with
exponential backoff to the pipeline's _call_llm helper. Increase
default max_tokens from 2000 to 4096.
Process book-1-chapter-05 via Gemini free tier — 1 new entity
extracted (necessaries-conveniencies-and-amusements-of-life),
41 existing entities correctly skipped by dedup. Canonical set
now at 42 unique entities.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Restructure entity storage from per-chapter subdirectories to a flat
canonical set in output/entities/. Each entity exists as a single file;
duplicates across chapters are detected by slug collision and skipped
(first occurrence wins). Chapter views use {{ include }} transclusion
to reference shared entity files.
Add @{existing_entities} macro to extract-entities template so the LLM
knows which entities already exist and focuses on genuinely new ones.
Refactor _call_llm() from _execute_llm() for callers that handle their
own file I/O. 41 unique entities from 4 chapters (2 duplicates removed).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Comprehensive walkthrough covering schema design, prompt templates,
artifact population, pipeline usage, LLM integration, git history
tracking, metrics, and how to complete the remaining 31 chapters.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
