Document canonicalization and consolidation milestone

- preserve Working Protocol Synthesizer V0 as comparison baseline
- introduce deterministic canonicalization stage
- define semantic consolidator responsibilities
- clarify Canonical Meeting Knowledge generation
- document source-language output policy
- align roadmap, architecture and experiment log

PROJECT_KNOWLEDGE.md

@@ -30,7 +30,8 @@ Experimental/prototype:
 
 Planned:
 
-- Consolidation of extraction results.
+- Deterministic Canonicalizer for extraction results.
+- Semantic Consolidator for evidence-preserving semantic merging.
 - Canonical Meeting Knowledge implementation as the semantic source of truth.
 - Final Working Protocol / Arbeitsprotokoll, Distribution Protocol /
   Verteilerprotokoll and Knowledge Objects / Wissensdatenbankeintrag renderers.
@@ -40,7 +41,7 @@ Planned:
 ```text
 src/meeting_lab/
   chunking/        technical transcript chunking
-  consolidation/   planned merge/consolidation area
+  consolidation/   planned canonicalization/consolidation area
   extraction/      current local LLM extraction flow
   io/              lightweight file and JSON helpers
   llm/             Ollama and prompt support
@@ -112,10 +113,33 @@ Current Prompt Version 2 decision baseline:
 
 ## Canonical Knowledge Architecture
 
+The next documented pipeline milestone is:
+
+```text
+Chunk Extractions
+  -> Deterministic Canonicalizer
+  -> Semantic Consolidator
+  -> Canonical Meeting Knowledge
+  -> Output View Renderers
+```
+
+The Deterministic Canonicalizer is planned Python code with no LLM. It should
+validate and normalize extraction objects, assign stable source references and
+IDs, normalize category names and basic field structure, perform only safe
+deterministic cleanup, optionally group exact duplicates, and preserve all
+source evidence. It must not perform uncertain semantic merging.
+
+The Semantic Consolidator is planned local-LLM work. It should merge
+semantically equivalent statements, group content by topic, preserve evidence
+from all contributing chunks, mark contradictions and uncertainty, separate
+durable information from transient discussion, and produce Canonical Meeting
+Knowledge. It does not directly write a protocol.
+
 Canonical Meeting Knowledge is the planned semantic intermediate model and
-future single source of truth. It should preserve topics, facts, decisions,
-action items, open questions, positions, technical details, rationale,
-uncertainty, contradictions and source evidence.
+future single source of truth. It should be structured, preferably JSON, and
+preserve topics, facts, decisions, action items, open questions, positions,
+technical details, rationale, uncertainty, contradictions and source evidence.
+It is not itself a prose protocol.
 
 Output views are planned as independent renderings from that canonical model:
 
@@ -129,6 +153,10 @@ Output views are planned as independent renderings from that canonical model:
 The current `meeting_protocol.md` builder is an interim technical validation
 tool, not the final output-view architecture.
 
+Rendered protocol output should normally use the dominant language of the
+source transcript or consolidated meeting knowledge unless an explicit output
+language is requested.
+
 ## Current Limitations
 
 - Discussion Blocks are documented as a stable semantic unit but are not yet a
@@ -136,7 +164,8 @@ tool, not the final output-view architecture.
 - Topic segmentation exists as prototype tooling, not a stable pipeline stage.
 - Extraction is still a combined current flow, even though separate extractors
   are the intended architecture.
-- Consolidation is not implemented.
+- Deterministic Canonicalizer is documented but not implemented.
+- Semantic Consolidator is documented but not implemented.
 - Canonical Meeting Knowledge is documented but not implemented.
 - Final output views are documented but not implemented.
 - Most prompt files are placeholders except the common and decision prompts.
@@ -147,5 +176,5 @@ tool, not the final output-view architecture.
 Stabilize repeatable local extraction evaluation before broadening the pipeline:
 expand Gold Standard coverage by category, keep one-chunk extraction as the
 baseline, and use small prompt experiments with immediate non-regression checks.
-After extraction behavior is stable enough, implement consolidation with
-evidence retention as the next major pipeline stage.
+After extraction behavior is stable enough, implement the Deterministic
+Canonicalizer first, then the Semantic Consolidator with evidence retention.