tegwick
2d45425b25
Implements the L3 relation graph layer — a directed graph of (Subject,
Predicate, Object) triplets annotated with VSM channel codes and feedback
roles. Triplets are authored as markdown files under output/relations/,
parsed into RelationMeta dataclasses, and analysed with networkx.
New modules:
- markitect/infospace/relation_models.py — RelationMeta dataclass +
RELATION_TYPES controlled vocabulary (15 relation classes → VSM codes)
- markitect/infospace/relation_parser.py — parse_relation_file() and
parse_relations_directory()
New schema: examples/infospace-with-history/schemas/relation-schema-v1.0.md
— file naming convention, required sections, controlled vocabulary table
15 seed relation files covering the three core WoN feedback loops:
- Capital Accumulation loop (positive reinforcement, S1/S3)
- Market Price Balancing loop (negative feedback, S2/S3)
- Market Extent mutual dependency (S1/S2)
Plus structural relations: wages regulation, rent residual, price
decomposition, invisible hand coordination
CLI: markitect infospace relations [--entity SLUG] [--vsm FILTER]
[--loops] [--stats]
- Builds directed graph from parsed files
- Detects feedback loops via nx.simple_cycles()
- 6 loops found from 15 seed relations (3 intended + 3 emergent)
- --stats aggregates by VSM system code (strips parentheticals)
Config: InfospaceConfig gains relations_dir (default output/relations)
infospace.yaml: schemas.relation references relation-schema-v1.0.md
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
"""
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Data models for L3 relation triplets.
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A relation triplet is the fundamental unit of the relation graph:
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Subject --[Predicate]--> Object
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Each triplet is stored as a markdown file in ``output/relations/``.
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"""
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from dataclasses import dataclass, field
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from typing import List, Optional
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# Controlled relation vocabulary — maps semantic class to VSM channel
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RELATION_TYPES = {
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"enables": "S1 → S1",
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"constrains": "S1 ← S1",
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"regulates": "S3 → S1",
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"is regulated by": "S1 ← S3",
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"coordinates": "S2",
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"produces": "S1",
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"consumes": "S1",
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"monitors": "S3*",
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"audits": "S3*",
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"adapts to": "S4",
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"anticipates": "S4",
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"defines": "S5 → any",
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"is defined by": "any ← S5",
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"contradicts": "any",
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"tensions with": "any",
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}
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@dataclass
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class RelationMeta:
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"""Structured metadata for a single relation triplet.
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Attributes:
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slug: Unique identifier, e.g.
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``division_of_labour--constrains--market_extent``
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subject: Human-readable title of the subject entity
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subject_slug: Slug of the subject entity (links to L1)
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predicate: Human-readable predicate phrase, e.g. "limited by"
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object: Human-readable title of the object entity
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object_slug: Slug of the object entity (links to L1)
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relation_type: Semantic class from the controlled vocabulary
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vsm_channel: VSM systems involved, e.g. "S1 → S2"
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evidence: Source text quote or chapter reference
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feedback_role: Description of role in a feedback loop (if any)
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source_path: Absolute path to the ``.md`` file
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"""
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slug: str
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subject: str
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subject_slug: str
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predicate: str
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object: str
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object_slug: str
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relation_type: str
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vsm_channel: str
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evidence: str = ""
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feedback_role: str = ""
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source_path: str = ""
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@property
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def is_feedback_member(self) -> bool:
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"""True if this relation participates in a named feedback loop."""
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return bool(self.feedback_role.strip())
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def edge(self) -> tuple:
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"""Return a (subject_slug, object_slug, predicate) edge tuple."""
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return (self.subject_slug, self.object_slug, self.predicate)
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