refactor: improve web results analysis prompt

Apply llm-findings.md recommendations:

Web results analysis (temp 0.0):
- Add ANALYSIS STEPS for chain-of-thought reasoning
- Strict RULES section with negative constraints:
  - "Do NOT suggest pages with insufficient info"
  - "Do NOT invent entities not mentioned"
  - "Do NOT suggest paths outside taxonomy"
- Conservative approach: quality over quantity
- Removed "be INCLUSIVE" guidance (caused over-suggestion)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

src/services/consolidation_service.py

@@ -410,13 +410,19 @@ This is a PERSONAL knowledge base using Schema.org-aligned taxonomy that capture
 - Projects: Work projects, personal projects (Schema.org: Project)
 - Reference: General knowledge, how-tos (Custom extension)
 
-Identify information worth documenting:
-1. New topics/people/things that deserve their own wiki page
-2. Facts that could enhance existing pages
-3. Entities (people, places, things, concepts) for the knowledge graph
+ANALYSIS STEPS:
+1. Read each web result carefully for substantive, factual content
+2. Identify genuinely novel information not likely already known
+3. Match topics to appropriate taxonomy categories
+4. Generate valid paths following the exact format below
 
-Be INCLUSIVE - if someone searched for it, it's likely worth documenting.
-Personal information is just as valuable as technical information.
+RULES:
+- Do NOT suggest pages for topics with insufficient information in results
+- Do NOT invent entities not explicitly mentioned in results
+- Do NOT suggest paths that don't match the taxonomy exactly
+- Do NOT suggest generic or vague page topics
+- Be CONSERVATIVE - fewer high-quality suggestions is better than many low-quality ones
+- ONLY suggest documentation for substantive, specific information
 
 **CRITICAL: Use ONLY these Schema.org-aligned path prefixes (case-sensitive):**
 
@@ -462,11 +468,12 @@ Return ONLY valid JSON:
 JSON:"""
 
         try:
-            # Call Ollama for analysis
+            # Call Ollama for analysis (temperature=0.0 for consistent classification)
             response = await self.ollama.generate_text(
                 prompt=prompt,
                 model=self.settings.ollama_model,
-                stream=False
+                stream=False,
+                temperature=0.0
             )
 
             if not response: