feat(infospace): add L2 entity classification with type × VSM matrix (S2.9)

Implements the L2 typed-entities layer — each entity is assigned an
Entity Type (Element, Process, Relation, Principle, Institution) and a
VSM System (S1–S5) by an LLM, with one-sentence rationales for each.

New modules:
- markitect/infospace/classification.py — EntityClassification dataclass
  + ENTITY_TYPES / VSM_SYSTEMS controlled vocabularies
- markitect/infospace/classification_io.py — write/read classification
  files (YAML frontmatter + markdown body, mirrors evaluation_io)
- markitect/infospace/classifier.py — build_classification_prompt(),
  parse_classification_response(), run_entity_classification(); batch
  runner writes files incrementally (same resumable pattern as evaluate)

CLI: markitect infospace classify [--entity SLUG] [--provider P] [--model M]
  - Incremental skip: checks output/classifications/ for existing files
  - Defaults to openrouter provider; 2000 max_tokens (Gemini 2.5 Flash
    uses ~787 thinking tokens, so 800 was too low)

CLI: markitect infospace classify-summary [--update-metrics]
  - Entity type counts + VSM system counts with percentages
  - 5 × 6 type × VSM matrix (spots structural blind spots at a glance)
  - --update-metrics writes type_distribution, type_entropy,
    vsm_type_matrix_cells to metrics.yaml

Config: InfospaceConfig gains classifications_dir (default output/classifications)
Schema: schemas/typed-entity-schema-v1.0.md — type/VSM vocabulary tables,
  rationale format rules, validation rules, metrics enabled at L2
infospace.yaml: schemas.typed_entity references typed-entity-schema-v1.0.md

Seed classifications (3): division_of_labour (Process/S1),
  natural_price_as_central_price (Principle/S2),
  invisible_hand_mechanism (Principle/S4)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

markitect/infospace/classification.py

@@ -0,0 +1,64 @@
+"""
+Data models for entity classification (L2 typed entities).
+
+Each entity is assigned an Entity Type (what kind of thing it is) and a
+VSM System (which control layer it inhabits).  Both assignments come with
+a one-sentence rationale from the LLM, stored alongside the classification.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Any, Dict, Optional
+
+
+#: Controlled vocabulary for entity types.
+ENTITY_TYPES = ["Element", "Process", "Relation", "Principle", "Institution"]
+
+#: Controlled vocabulary for VSM system assignments.
+VSM_SYSTEMS = ["S1", "S2", "S3", "S3*", "S4", "S5"]
+
+
+@dataclass
+class EntityClassification:
+    """L2 classification for a single entity."""
+
+    entity_slug: str
+    entity_type: str          # one of ENTITY_TYPES
+    vsm_system: str           # one of VSM_SYSTEMS
+    type_rationale: str = ""  # one sentence
+    vsm_rationale: str = ""   # one sentence
+    classified_by: str = ""   # model name
+    classified_at: Optional[datetime] = None
+
+    def to_dict(self) -> Dict[str, Any]:
+        d: Dict[str, Any] = {
+            "entity_slug": self.entity_slug,
+            "entity_type": self.entity_type,
+            "vsm_system": self.vsm_system,
+        }
+        if self.type_rationale:
+            d["type_rationale"] = self.type_rationale
+        if self.vsm_rationale:
+            d["vsm_rationale"] = self.vsm_rationale
+        if self.classified_by:
+            d["classified_by"] = self.classified_by
+        if self.classified_at is not None:
+            d["classified_at"] = self.classified_at.isoformat()
+        return d
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "EntityClassification":
+        classified_at: Optional[datetime] = None
+        if "classified_at" in data:
+            classified_at = datetime.fromisoformat(data["classified_at"])
+        return cls(
+            entity_slug=data["entity_slug"],
+            entity_type=data["entity_type"],
+            vsm_system=data["vsm_system"],
+            type_rationale=data.get("type_rationale", ""),
+            vsm_rationale=data.get("vsm_rationale", ""),
+            classified_by=data.get("classified_by", ""),
+            classified_at=classified_at,
+        )