73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
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"""
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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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