feat(infospace): add entity metadata parser (S1.1)

Extract section-tree algorithm from SchemaGenerator into standalone
core/section_tree.py and build markitect/infospace/ package with
EntityMeta dataclass and parse_entity_file/parse_entity_directory.
Foundation for schema compliance, coverage, and granularity metrics.

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

markitect/infospace/models.py

@@ -0,0 +1,53 @@
+"""
+Data models for infospace entity metadata.
+"""
+
+from dataclasses import dataclass, field, asdict
+from typing import Any, Dict, List
+
+
+@dataclass
+class EntityMeta:
+    """Structured metadata extracted from a single entity markdown file.
+
+    The parser populates every field it can find; missing optional
+    sections are left as empty strings (validation is a separate step).
+    """
+
+    # Identity
+    slug: str
+    title: str
+    h1_raw: str  # verbatim H1 text before any normalisation
+
+    # Section contents (plain text, empty string if section missing)
+    definition: str = ""
+    source_chapter: str = ""
+    context: str = ""
+    domain: str = ""
+    original_wording: str = ""
+    modern_interpretation: str = ""
+
+    # Derived flags
+    h1_is_title_case: bool = False
+    has_original_wording: bool = False
+
+    # Metrics-ready numbers
+    definition_word_count: int = 0
+    total_word_count: int = 0
+
+    # All H2 section slugs found (preserves order)
+    section_slugs: List[str] = field(default_factory=list)
+
+    # Source file path (as string for serialisation)
+    source_path: str = ""
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Serialise to a plain dictionary."""
+        return asdict(self)
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "EntityMeta":
+        """Deserialise from a plain dictionary."""
+        known_fields = {f.name for f in cls.__dataclass_fields__.values()}
+        filtered = {k: v for k, v in data.items() if k in known_fields}
+        return cls(**filtered)