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

docs/pipeline.md

@@ -33,7 +33,10 @@ Topic Segmentation
 Specialized Extraction
         │
         ▼
-Consolidation
+Deterministic Canonicalization
+        │
+        ▼
+Semantic Consolidation
         │
         ▼
 Canonical Meeting Knowledge
@@ -267,35 +270,41 @@ Prototype exists as a combined extractor.
 
 ---
 
-# Stage 6 – Consolidation
+# Stage 6 – Deterministic Canonicalization
 
 ## Purpose
 
-Merge analysis results originating from different discussion segments.
+Normalize raw chunk extraction JSON into stable canonical extraction objects
+without changing uncertain semantics.
 
 ## Input
 
-Extraction results.
+Chunk extraction JSON files.
 
 ## Output
 
-Unified topic representation.
+Validated canonical extraction objects with stable source references and IDs.
 
 ## Responsibilities
 
-- Merge duplicates
-- Merge complementary information
-- Preserve contradictions
-- Separate positions from decisions
-- Combine related todos
+- Validate extraction objects
+- Normalize category names
+- Normalize basic field structure
+- Assign stable source references and IDs
+- Preserve all source evidence
+- Perform only safe deterministic cleanup
+- Group exact duplicates where unambiguous
+
+## Must Not
+
+- Use an LLM
+- Perform uncertain semantic merging
+- Infer missing information
+- Drop source evidence
 
 ## Processing Type
 
-Hybrid
-
-Deterministic wherever possible.
-
-LLM support only if necessary.
+Deterministic Python
 
 ## Current Status
 
@@ -303,13 +312,55 @@ Planned
 
 ---
 
-# Stage 7 – Canonical Meeting Knowledge
+# Stage 7 – Semantic Consolidation
+
+## Purpose
+
+Merge canonicalized extraction objects into an evidence-preserving semantic
+meeting representation.
+
+## Input
+
+Canonicalized extraction objects.
+
+## Output
+
+Canonical Meeting Knowledge.
+
+## Responsibilities
+
+- Merge semantically equivalent statements
+- Group content by topic
+- Preserve evidence from all contributing chunks
+- Mark contradictions and uncertainty
+- Separate durable information from transient discussion
+- Reconcile category shifts where supported by evidence
+
+## Must Not
+
+- Directly write a protocol
+- Invent information
+- Drop conflicting evidence silently
+
+## Processing Type
+
+Local LLM, with deterministic pre/post-processing where useful.
+
+## Current Status
+
+Planned
+
+---
+
+# Stage 8 – Canonical Meeting Knowledge
 
 ## Purpose
 
 Produce the canonical semantic representation of one meeting.
 
 This representation is the single source of truth for all downstream outputs.
+It is a structured representation, preferably JSON, and is not itself a prose
+protocol.
 
 Example:
 
@@ -352,7 +403,7 @@ Planned
 
 ---
 
-# Stage 8 – Output View Rendering
+# Stage 9 – Output View Rendering
 
 ## Purpose
 
@@ -365,6 +416,7 @@ The planned output products are:
 - Knowledge Objects, rendered as a Knowledge-base Entry (`knowledge_entry.md`)
   and later stored in a structured format such as `knowledge_entry.json`
   (Wissensdatenbankeintrag)
+- Later additional views such as action lists
 
 Output rendering never performs additional analysis.
 
@@ -383,6 +435,10 @@ Completeness differs by output:
 - The Distribution Protocol optimizes for relevance and brevity.
 - Knowledge Objects optimize for durability and reuse.
 
+Rendered protocol language should normally match the dominant language of the
+source transcript or consolidated meeting knowledge unless an explicit output
+language is requested.
+
 ## Processing Type
 
 LLM
@@ -451,11 +507,16 @@ A processing stage may be replaced by another implementation as long as it prese
 
 ⬜ Specialized Extraction
 
-⬜ Consolidation
+⬜ Deterministic Canonicalization
+
+⬜ Semantic Consolidation
 
 ⬜ Canonical Meeting Knowledge
 
 ⬜ Output View Rendering
 ```
 
-The immediate development focus is **Topic Segmentation**, as it provides the semantic structure on which all subsequent processing stages depend.
+The immediate architecture focus is the Deterministic Canonicalizer followed by
+the Semantic Consolidator. These stages preserve source evidence, recover
+global context from independent chunk extractions and prepare Canonical Meeting
+Knowledge for parallel Output View rendering.