feat: Intelligence Pipeline v3 — full implementation

Multi-stage evidence-grounded inference architecture replacing the
monolithic 9B model extraction pipeline. CPU-first specialist services
handle routine extraction while the 9B vLLM model is preserved for
semantic adjudication of ambiguous cases.

Key components:
- Capability-aware inference gateway (OpenAI-compatible + Ollama)
- Endpoint registry with DB migrations and REST API
- Sentence-aware document segmenter (property tests)
- Deterministic financial parsing with offset integrity
- Symbol resolution with ambiguity detection
- Specialist service (GLiNER2, dynamic batching, K8s deployment)
- Company-specific sentiment (FinBERT, calibration)
- Retrieval-based novelty and duplicate detection
- Confidence calibration pipeline
- Deterministic routing engine (property tests)
- 9B adjudication layer with VRAM gating
- Stock-specific impact model (features, labels, baseline, trained)
- Pipeline orchestrator (state machine, queues, leases, feature flags)
- Bounded parallelism (async workers, semaphore, load shedding)
- Observability (tracing, metrics, alerts)
- Compatibility adapter (v3→v2 golden mapping tests)
- Shadow/canary promotion framework
- Active learning and fine-tuning pipeline

Test results: 1,161 tests pass, ruff lint clean.
All 282 spec tasks completed.

services/intelligence_pipeline_v3/schemas/__init__.py

@@ -0,0 +1,66 @@
+"""V3 annotation schema — entity, event, relation, sentiment, and evidence models."""
+
+from services.intelligence_pipeline_v3.schemas.annotations import (
+    AmbiguityMarker,
+    AmbiguityType,
+    AnnotatedDocument,
+    AnnotationMetadata,
+    CompanySentimentAnnotation,
+    DirectEffect,
+    EntityAnnotation,
+    EntityType,
+    EventAnnotation,
+    EventClass,
+    EvidenceSpanAnnotation,
+    InferredExposure,
+    NumericFactAnnotation,
+    PeriodAnnotation,
+    PeriodType,
+    RelationAnnotation,
+    RelationType,
+    SentimentLabel,
+)
+from services.intelligence_pipeline_v3.schemas.safety import (
+    SAFETY_CRITICAL_FIELDS,
+    SafetyCriticalField,
+    SafetyGateResult,
+    check_safety_gates,
+)
+from services.intelligence_pipeline_v3.schemas.validators import (
+    ValidationError as AnnotationValidationError,
+)
+from services.intelligence_pipeline_v3.schemas.validators import (
+    ValidationResult,
+    validate_annotation,
+)
+
+__all__ = [
+    # Annotation models
+    "AmbiguityMarker",
+    "AmbiguityType",
+    "AnnotatedDocument",
+    "AnnotationMetadata",
+    "CompanySentimentAnnotation",
+    "DirectEffect",
+    "EntityAnnotation",
+    "EntityType",
+    "EvidenceSpanAnnotation",
+    "EventAnnotation",
+    "EventClass",
+    "InferredExposure",
+    "NumericFactAnnotation",
+    "PeriodAnnotation",
+    "PeriodType",
+    "RelationAnnotation",
+    "RelationType",
+    "SentimentLabel",
+    # Safety
+    "SAFETY_CRITICAL_FIELDS",
+    "SafetyCriticalField",
+    "SafetyGateResult",
+    "check_safety_gates",
+    # Validators
+    "AnnotationValidationError",
+    "ValidationResult",
+    "validate_annotation",
+]