[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "nexus", "label": "Nexus", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Clearpath"}}, {"id": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aws", "label": "AWS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "kubernetes", "label": "Kubernetes", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Nexus development

PREVIOUS ASSISTANT: What else about Nexus?
USER: Let's dive into the caching layer part of Nexus.

[Assistant] {"topic": {"action": "subtopic", "label": "Nexus caching layer"}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "forge", "label": "Forge", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Ironforge"}}, {"id": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "angular", "label": "Angular", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "jenkins", "label": "Jenkins", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Forge development

PREVIOUS ASSISTANT: Thanks for the context about Forge.
USER: Some updates: Currently in alpha. The team has 15 developers. Launched in February 2026.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "forge", "text": "Currently in alpha", "speaker": "user"}, {"entity": "forge", "text": "The team has 15 developers", "speaker": "user"}, {"entity": "forge", "text": "Launched in February 2026", "speaker": "user"}]}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "nexus", "label": "Nexus", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at BlueShift Labs"}}, {"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Nexus development

PREVIOUS ASSISTANT: What else about Nexus?
USER: Can we talk about the API design for Nexus?

[Assistant] {"topic": {"action": "subtopic", "label": "Nexus API design"}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "mcgonagall", "label": "Minerva McGonagall", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "harry_potter", "label": "Harry Potter", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ron_weasley", "label": "Ron Weasley", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hagrid", "label": "Rubeus Hagrid", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "forbidden_forest", "label": "Forbidden Forest", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hogsmeade", "label": "Hogsmeade", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hogwarts", "label": "Hogwarts", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Harry Potter

PREVIOUS ASSISTANT: null
USER: In Harry Potter, flying broomstick sporting event. Rubeus Hagrid and Minerva McGonagall and Harry Potter participate. Happens at Hogsmeade, early in the story.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "quidditch_match", "label": "Quidditch Match", "type": "event", "layer": "UNIVERSAL", "attributes": {"description": "Flying broomstick sporting event", "temporal_marker": "early in the story"}, "facts": [], "existing_id": null}], "relations": [{"source": "hagrid", "target": "quidditch_match", "relation": "participated_in"}, {"source": "mcgonagall", "target": "quidditch_match", "relation": "participated_in"}, {"source": "harry_potter", "target": "quidditch_match", "relation": "participated_in"}, {"source": "quidditch_match", "target": "hogsmeade", "relation": "located_in"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "forge", "label": "Forge", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Clearpath"}}, {"id": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: null

PREVIOUS ASSISTANT: null
USER: OK let me think about that.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "draco_malfoy", "label": "Draco Malfoy", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "dumbledore", "label": "Albus Dumbledore", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mcgonagall", "label": "Minerva McGonagall", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hagrid", "label": "Rubeus Hagrid", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ministry_of_magic", "label": "Ministry of Magic", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hogwarts", "label": "Hogwarts", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Harry Potter

PREVIOUS ASSISTANT: Tell me more about Harry Potter.
USER: There's also Severus Snape who appears in the story. They interact with Rubeus Hagrid a lot.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "snape", "label": "Severus Snape", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "snape", "target": "hagrid", "relation": "part_of"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: null

PREVIOUS ASSISTANT: I've processed the document. Here's what I found:
USER: Here's a summary of the document I just read:

Architecture overview of Prism. Tech stack: Flask, Svelte, Next.js. Key contributors: Maria Rodriguez, Omar Hassan, Sarah Johnson.

[Assistant] {"topic": {"action": "changed", "label": "Prism development"}, "entities": [{"id": "prism", "label": "Prism", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "prism_architecture_doc", "label": "Prism Architecture Document", "type": "document", "layer": "PERSONAL", "attributes": {"description": "Architecture overview of Prism. Tech stack: Flask, Svelte, Next.js. Key contributors: Maria Rodrigue"}, "facts": [], "existing_id": null}], "relations": [{"source": "prism", "target": "flask", "relation": "uses_technology"}, {"source": "prism", "target": "svelte", "relation": "uses_technology"}, {"source": "prism", "target": "nextjs", "relation": "uses_technology"}, {"source": "prism_architecture_doc", "target": "prism", "relation": "documented_in"}, {"source": "maria_rodriguez", "target": "prism", "relation": "maintains"}, {"source": "omar_hassan", "target": "prism", "relation": "maintains"}, {"source": "sarah_johnson", "target": "prism", "relation": "maintains"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "helix", "label": "Helix", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at BlueShift Labs"}}, {"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Helix development

PREVIOUS ASSISTANT: null
USER: Esta mañana, we did a sprint review with Luca Rossi and Maria Rodriguez. Reviewed Q3 deliverables.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "sprint_review", "label": "Sprint Review", "type": "event", "layer": "PERSONAL", "attributes": {"description": "Reviewed Q3 deliverables", "temporal_marker": "esta mañana"}, "facts": [], "existing_id": null}], "relations": [{"source": "luca_rossi", "target": "sprint_review", "relation": "participated_in"}, {"source": "maria_rodriguez", "target": "sprint_review", "relation": "participated_in"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Clearpath team

PREVIOUS ASSISTANT: null
USER: Raj Kapoor is joining the team as full-stack developer to work on Orbit.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "orbit", "label": "Orbit", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as full-stack developer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "orbit", "target": "clearpath", "relation": "part_of"}, {"source": "raj_kapoor", "target": "orbit", "relation": "maintains"}, {"source": "raj_kapoor", "target": "clearpath", "relation": "works_at"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "compass", "label": "Compass", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Pinnacle Digital"}}, {"id": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Compass development

PREVIOUS ASSISTANT: null
USER: Oh, I forgot to mention — has 292 tables.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "firebase_db", "text": "Has 292 tables", "speaker": "user"}, {"entity": "nina_petrov", "text": "Has been on the project for 12 months", "speaker": "user"}]}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "pulse", "label": "Pulse", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Harbor Systems"}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "postgresql", "label": "PostgreSQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "docker", "label": "Docker", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Pulse development

PREVIOUS ASSISTANT: null
USER: We need to optimize the DB queries.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "the_db", "label": "The Db", "type": "technology", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "postgresql"}], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "vertex_app", "label": "Vertex", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Ironforge"}}, {"id": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "spring_boot", "label": "Spring Boot", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "postgresql", "label": "PostgreSQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Vertex development

PREVIOUS ASSISTANT: null
USER: We need to optimize the DB queries.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "the_db", "label": "The Db", "type": "technology", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "postgresql"}], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "vertex_app", "label": "Vertex", "type": "project", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Acme Corp team

PREVIOUS ASSISTANT: null
USER: I need to discuss the hiring plan specifically.

[Assistant] {"topic": {"action": "subtopic", "label": "Hiring Plan"}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "helix", "label": "Helix", "type": "project", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: BlueShift Labs team

PREVIOUS ASSISTANT: null
USER: I need to discuss the client onboarding specifically.

[Assistant] {"topic": {"action": "subtopic", "label": "Client Onboarding"}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "atlas", "label": "Atlas", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Vertex Solutions"}}, {"id": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Atlas development

PREVIOUS ASSISTANT: null
USER: Correction: Marco Silva left the company. Ahmed Farid replaced them.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "marco_silva", "text": "Left the company", "speaker": "user"}]}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "toronto", "label": "Toronto", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "painting", "label": "Painting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "running", "label": "Running", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Personal life

PREVIOUS ASSISTANT: null
USER: Oh I forgot, the commute is 31 minutes.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "toronto", "text": "The commute is 31 minutes", "speaker": "user"}, {"entity": "toronto", "text": "Great parks", "speaker": "user"}]}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: null

PREVIOUS ASSISTANT: null
USER: I'm working on Apex, a marketplace built with React and Express. We're using Redis for the database. The team lead is Thomas Müller.

[Assistant] {"topic": {"action": "changed", "label": "Apex development"}, "entities": [{"id": "apex", "label": "Apex", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "apex", "target": "harbor_systems", "relation": "part_of"}, {"source": "apex", "target": "react", "relation": "uses_technology"}, {"source": "apex", "target": "express", "relation": "uses_technology"}, {"source": "apex", "target": "redis", "relation": "uses_technology"}, {"source": "thomas_muller", "target": "harbor_systems", "relation": "works_at"}, {"source": "thomas_muller", "target": "apex", "relation": "maintains"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "leto_atreides", "label": "Duke Leto Atreides", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "jessica", "label": "Lady Jessica", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "baron_harkonnen", "label": "Baron Harkonnen", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "stilgar", "label": "Stilgar", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "paul_atreides", "label": "Paul Atreides", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "duncan_idaho", "label": "Duncan Idaho", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "chani", "label": "Chani", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "giedi_prime", "label": "Giedi Prime", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "arrakis", "label": "Arrakis", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "caladan", "label": "Caladan", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Dune

PREVIOUS ASSISTANT: null
USER: In Dune, paul rides a sandworm for the first time. Paul Atreides and Stilgar and Chani participate. Happens at Caladan, near the end.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "riding_sandworm", "label": "Sandworm Ride", "type": "event", "layer": "UNIVERSAL", "attributes": {"description": "Paul rides a sandworm for the first time", "temporal_marker": "near the end"}, "facts": [], "existing_id": null}], "relations": [{"source": "paul_atreides", "target": "riding_sandworm", "relation": "participated_in"}, {"source": "stilgar", "target": "riding_sandworm", "relation": "participated_in"}, {"source": "chani", "target": "riding_sandworm", "relation": "participated_in"}, {"source": "riding_sandworm", "target": "caladan", "relation": "located_in"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: null

PREVIOUS ASSISTANT: Let me know if you need anything else.
USER: Hablamos.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "prism", "label": "Prism", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Harbor Systems"}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aws", "label": "AWS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "kubernetes", "label": "Kubernetes", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Prism development

PREVIOUS ASSISTANT: Got it, tell me more about Prism.
USER: David Kim arrancó esta semana como DevOps engineer. Y ahora usamos Bootstrap.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "bootstrap", "label": "Bootstrap", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as DevOps engineer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "prism", "target": "bootstrap", "relation": "uses_technology"}, {"source": "david_kim", "target": "prism", "relation": "maintains"}, {"source": "david_kim", "target": "harbor_systems", "relation": "works_at"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "berlin", "label": "Berlin", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "woodworking", "label": "Woodworking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Personal life

PREVIOUS ASSISTANT: null
USER: We had graduation two weeks ago. Nina Petrov and Elena Volkov were there. Ceremony at the local university.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "graduation", "label": "Graduation", "type": "event", "layer": "PERSONAL", "attributes": {"description": "Ceremony at the local university", "temporal_marker": "two weeks ago"}, "facts": [], "existing_id": null}], "relations": [{"source": "nina_petrov", "target": "graduation", "relation": "participated_in"}, {"source": "elena_volkov", "target": "graduation", "relation": "participated_in"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "forge", "label": "Forge", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at NovaTech"}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aws", "label": "AWS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "jenkins", "label": "Jenkins", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Forge development

PREVIOUS ASSISTANT: Got it, tell me more about Forge.
USER: We brought in Bob Martinez as DevOps engineer and started using Python for the project.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "python_lang", "label": "Python", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as DevOps engineer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "forge", "target": "python_lang", "relation": "uses_technology"}, {"source": "bob_martinez", "target": "forge", "relation": "maintains"}, {"source": "bob_martinez", "target": "novatech", "relation": "works_at"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "beacon", "label": "Beacon", "type": "project", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Harbor Systems team

PREVIOUS ASSISTANT: null
USER: About the hiring plan — where are we on that?

[Assistant] {"topic": {"action": "subtopic", "label": "Hiring Plan"}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "barcelona", "label": "Barcelona", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "cycling", "label": "Cycling", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gardening", "label": "Gardening", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Personal life

PREVIOUS ASSISTANT: null
USER: Mi wife quiere ir al parque el fin de semana.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "my_wife", "label": "My Wife", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "elena_volkov"}], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: null

PREVIOUS ASSISTANT: I've processed the document. Here's what I found:
USER: Here's a summary of the document I just read:

Architecture overview of Nexus. Tech stack: PostgreSQL, Flask, MySQL, Svelte. Key contributors: James O'Brien, Luca Rossi, Julia Santos.

[Assistant] {"topic": {"action": "changed", "label": "Nexus development"}, "entities": [{"id": "nexus", "label": "Nexus", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "postgresql", "label": "PostgreSQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "nexus_architecture_doc", "label": "Nexus Architecture Document", "type": "document", "layer": "PERSONAL", "attributes": {"description": "Architecture overview of Nexus. Tech stack: PostgreSQL, Flask, MySQL, Svelte. Key contributors: Jame"}, "facts": [], "existing_id": null}], "relations": [{"source": "nexus", "target": "postgresql", "relation": "uses_technology"}, {"source": "nexus", "target": "flask", "relation": "uses_technology"}, {"source": "nexus", "target": "mysql", "relation": "uses_technology"}, {"source": "nexus", "target": "svelte", "relation": "uses_technology"}, {"source": "nexus_architecture_doc", "target": "nexus", "relation": "documented_in"}, {"source": "james_obrien", "target": "nexus", "relation": "maintains"}, {"source": "luca_rossi", "target": "nexus", "relation": "maintains"}, {"source": "julia_santos", "target": "nexus", "relation": "maintains"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "gollum", "label": "Gollum", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gandalf", "label": "Gandalf", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "minas_tirith", "label": "Minas Tirith", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Lord of the Rings

PREVIOUS ASSISTANT: null
USER: What's the significance of Minas Tirith?

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "summit", "label": "Summit", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Harbor Systems"}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Summit development

PREVIOUS ASSISTANT: null
USER: Can you remind me what database Summit uses?

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: null

PREVIOUS ASSISTANT: null
USER: This semester I'm enrolled in Organic Chemistry. Professor Nakamura is teaching it. So far we've covered Overfitting.

[Assistant] {"topic": {"action": "changed", "label": "Organic Chemistry course"}, "entities": [{"id": "organic_chemistry", "label": "Organic Chemistry", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "prof_nakamura", "label": "Professor Nakamura", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "overfitting", "label": "Overfitting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "overfitting", "target": "organic_chemistry", "relation": "part_of"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: null

PREVIOUS ASSISTANT: null
USER: Let me introduce myself. I'm from Paris, my wife James O'Brien and I live there. I'm really into running.

[Assistant] {"topic": {"action": "changed", "label": "Personal life"}, "entities": [{"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Is the user's wife", "speaker": "user"}], "existing_id": null}, {"id": "paris", "label": "Paris", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "running", "label": "Running", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "james_obrien", "target": "paris", "relation": "located_in"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "pulse", "label": "Pulse", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Acme Corp"}}, {"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Pulse development

PREVIOUS ASSISTANT: null
USER: We're not using Supabase anymore, we migrated to Spring Boot.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "spring_boot", "label": "Spring Boot", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "pulse", "target": "spring_boot", "relation": "uses_technology"}], "facts": [{"entity": "pulse", "text": "Migrated from Supabase to Spring Boot", "speaker": "user"}]}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "summit", "label": "Summit", "type": "project", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Vertex Solutions team

PREVIOUS ASSISTANT: null
USER: We had a quarterly review on Monday with Julia Santos and Nina Petrov and Aisha Patel. Reviewed performance and set goals.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "quarterly_review", "label": "Quarterly Review", "type": "event", "layer": "PERSONAL", "attributes": {"description": "Reviewed performance and set goals", "temporal_marker": "on Monday"}, "facts": [], "existing_id": null}], "relations": [{"source": "julia_santos", "target": "quarterly_review", "relation": "participated_in"}, {"source": "nina_petrov", "target": "quarterly_review", "relation": "participated_in"}, {"source": "aisha_patel", "target": "quarterly_review", "relation": "participated_in"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "organic_chemistry", "label": "Organic Chemistry", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_garcia", "label": "Professor García", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "regularization", "label": "Regularization", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "transformer_arch", "label": "Transformer Architecture", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gradient_descent", "label": "Gradient Descent", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Organic Chemistry course

PREVIOUS ASSISTANT: null
USER: Can you explain Regularization again?

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: null

PREVIOUS ASSISTANT: null
USER: Hablemos de Dune. Me enganché con Baron Harkonnen y Lady Jessica. Caladan es un escenario increíble.

[Assistant] {"topic": {"action": "changed", "label": "Dune"}, "entities": [{"id": "baron_harkonnen", "label": "Baron Harkonnen", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "jessica", "label": "Lady Jessica", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "caladan", "label": "Caladan", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "baron_harkonnen", "target": "caladan", "relation": "located_in"}, {"source": "jessica", "target": "caladan", "relation": "located_in"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "catalyst", "label": "Catalyst", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Pinnacle Digital"}}, {"id": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Catalyst development

PREVIOUS ASSISTANT: null
USER: Esta mañana, we did a sprint review with Ahmed Farid. Reviewed Q2 deliverables.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "sprint_review", "label": "Sprint Review", "type": "event", "layer": "PERSONAL", "attributes": {"description": "Reviewed Q2 deliverables", "temporal_marker": "esta mañana"}, "facts": [], "existing_id": null}], "relations": [{"source": "ahmed_farid", "target": "sprint_review", "relation": "participated_in"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "prism", "label": "Prism", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Meridian Software"}}, {"id": "meridian_software", "label": "Meridian Software", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "docker", "label": "Docker", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Prism development

PREVIOUS ASSISTANT: I see, Prism is built with Next.js.
USER: Quick update on Prism: Has 61 employees.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "meridian_software", "text": "Has 61 employees", "speaker": "user"}]}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "compass", "label": "Compass", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Skyline Tech"}}, {"id": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gcp", "label": "GCP", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "terraform", "label": "Terraform", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "github_actions", "label": "GitHub Actions", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "docker", "label": "Docker", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bootstrap", "label": "Bootstrap", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "tailwind", "label": "Tailwind CSS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Compass development

PREVIOUS ASSISTANT: null
USER: Qué framework usamos en el frontend de Compass?

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "berlin", "label": "Berlin", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gardening", "label": "Gardening", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Personal life

PREVIOUS ASSISTANT: null
USER: Oh, and my dad Omar Hassan lives nearby too.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Is the user's dad", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "omar_hassan", "target": "berlin", "relation": "located_in"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "summit", "label": "Summit", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Pinnacle Digital"}}, {"id": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: changed (medium confidence — verify)
CURRENT TOPIC: Summit development

PREVIOUS ASSISTANT: Anything else about Summit?
USER: Switching gears — I went to New York last weekend. Amazing trip.

[Assistant] {"topic": {"action": "changed", "label": "Travel"}, "entities": [{"id": "new_york", "label": "New York", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "bariloche", "label": "Bariloche", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "running", "label": "Running", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Personal life

PREVIOUS ASSISTANT: null
USER: What neighborhood do I live in?

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "onyx", "label": "Onyx", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Harbor Systems"}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "postgresql", "label": "PostgreSQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Some project

PREVIOUS ASSISTANT: I've updated the information.
USER: That works for me.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "frodo", "label": "Frodo Baggins", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "boromir", "label": "Boromir", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "legolas", "label": "Legolas", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gimli", "label": "Gimli", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mordor", "label": "Mordor", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "isengard", "label": "Isengard", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rivendell", "label": "Rivendell", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Lord of the Rings

PREVIOUS ASSISTANT: null
USER: The part where Destruction of the Ring happens is incredible. Frodo Baggins, Legolas are all involved. It takes place at Mordor.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "destruction_ring", "label": "Destruction of the Ring", "type": "event", "layer": "UNIVERSAL", "attributes": {"description": "The One Ring is destroyed in Mount Doom", "temporal_marker": "in the middle of the book"}, "facts": [], "existing_id": null}], "relations": [{"source": "frodo", "target": "destruction_ring", "relation": "participated_in"}, {"source": "legolas", "target": "destruction_ring", "relation": "participated_in"}, {"source": "destruction_ring", "target": "mordor", "relation": "located_in"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "zenith", "label": "Zenith", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at NovaTech"}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gcp", "label": "GCP", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "jenkins", "label": "Jenkins", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Zenith development

PREVIOUS ASSISTANT: null
USER: Our project is almost done with the migration.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "our_product", "label": "Our Product", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "zenith"}], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "pulse", "label": "Pulse", "type": "project", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Harbor Systems team

PREVIOUS ASSISTANT: null
USER: The client wants a meeting this week.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "the_client", "label": "The Client", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "pinnacle_digital"}], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "barcelona", "label": "Barcelona", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "running", "label": "Running", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Personal life

PREVIOUS ASSISTANT: null
USER: Correction — Carlos Mendez changed jobs. They're now a chef.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "carlos_mendez", "text": "Carlos Mendez changed jobs. They're now a chef.", "speaker": "user"}]}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "atlas", "label": "Atlas", "type": "project", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Acme Corp team

PREVIOUS ASSISTANT: null
USER: How many people are on the team?

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "prism", "label": "Prism", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at BlueShift Labs"}}, {"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gcp", "label": "GCP", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "github_actions", "label": "GitHub Actions", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Prism development

PREVIOUS ASSISTANT: Got it, tell me more about Prism.
USER: Ahmed Farid arrancó esta semana como designer. Y ahora usamos Go.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "go_lang", "label": "Go", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as designer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "prism", "target": "go_lang", "relation": "uses_technology"}, {"source": "ahmed_farid", "target": "prism", "relation": "maintains"}, {"source": "ahmed_farid", "target": "blueshift_labs", "relation": "works_at"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "bariloche", "label": "Bariloche", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gardening", "label": "Gardening", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Personal life

PREVIOUS ASSISTANT: null
USER: When did I say I started gardening?

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "catalyst", "label": "Catalyst", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Acme Corp"}}, {"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: changed (medium confidence — verify)
CURRENT TOPIC: Catalyst development

PREVIOUS ASSISTANT: Anything else about Catalyst?
USER: Bueno, cambiando de tema. Fui a New York el finde. Increíble.

[Assistant] {"topic": {"action": "changed", "label": "Travel"}, "entities": [{"id": "new_york", "label": "New York", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "toronto", "label": "Toronto", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "woodworking", "label": "Woodworking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Personal life

PREVIOUS ASSISTANT: null
USER: OK, let's talk about work. I need to set up Echo with Nuxt.

[Assistant] {"topic": {"action": "changed", "label": "Echo development"}, "entities": [{"id": "echo_app", "label": "Echo", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "echo_app", "target": "nuxt", "relation": "uses_technology"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Work

PREVIOUS ASSISTANT: Here's what I found about that.
USER: Sure, let's do that.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "pulse", "label": "Pulse", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at NovaTech"}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Pulse development

PREVIOUS ASSISTANT: Anything else about Pulse?
USER: Anyway, enough about work. I've been getting into cycling lately.

[Assistant] {"topic": {"action": "changed", "label": "Travel"}, "entities": [{"id": "barcelona", "label": "Barcelona", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "orbit", "label": "Orbit", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Vertex Solutions"}}, {"id": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Orbit development

PREVIOUS ASSISTANT: I see, Orbit is built with Svelte.
USER: Just so you know, has 15168 monthly active users.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "orbit", "text": "Has 15168 monthly active users", "speaker": "user"}]}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: null

PREVIOUS ASSISTANT: null
USER: Hey, I'm part of the team at NovaTech. We have a client called Skyline Tech. Key people: Ahmed Farid is full-stack developer, Yuki Tanaka is mobile developer.

[Assistant] {"topic": {"action": "changed", "label": "NovaTech team"}, "entities": [{"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as full-stack developer", "speaker": "user"}], "existing_id": null}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as mobile developer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "novatech", "target": "skyline_tech", "relation": "serves"}, {"source": "ahmed_farid", "target": "novatech", "relation": "works_at"}, {"source": "yuki_tanaka", "target": "novatech", "relation": "works_at"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Acme Corp team

PREVIOUS ASSISTANT: Tell me more about Acme Corp.
USER: Update: Contract is worth $236k.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "blueshift_labs", "text": "Contract is worth $236k", "speaker": "user"}]}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "london", "label": "London", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "knitting", "label": "Knitting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gardening", "label": "Gardening", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Personal life

PREVIOUS ASSISTANT: null
USER: My grandfather is Bob Martinez, they live in London too.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Is the user's grandfather", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "bob_martinez", "target": "london", "relation": "located_in"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "prism", "label": "Prism", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Acme Corp"}}, {"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "spring_boot", "label": "Spring Boot", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Prism development

PREVIOUS ASSISTANT: Got it, so Prism uses Spring Boot and Nuxt.
USER: Un update de Prism: Has 17551 monthly active users.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "prism", "text": "Has 17551 monthly active users", "speaker": "user"}]}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "compass", "label": "Compass", "type": "project", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: BlueShift Labs team

PREVIOUS ASSISTANT: null
USER: What's Raj Kapoor's role again?

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "forge", "label": "Forge", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at NovaTech"}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "postgresql", "label": "PostgreSQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "docker", "label": "Docker", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: subtopic of Forge development (medium confidence — verify)
CURRENT TOPIC: Forge development

PREVIOUS ASSISTANT: What else about Forge?
USER: The search functionality is the next thing I need to figure out.

[Assistant] {"topic": {"action": "subtopic", "label": "Forge search functionality"}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "apex", "label": "Apex", "type": "project", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: BlueShift Labs team

PREVIOUS ASSISTANT: null
USER: Let's focus on the client onboarding now.

[Assistant] {"topic": {"action": "subtopic", "label": "Client Onboarding"}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "apex", "label": "Apex", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Acme Corp"}}, {"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "angular", "label": "Angular", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Apex development

PREVIOUS ASSISTANT: Got it, so Apex uses Rails and Angular.
USER: Oh, I forgot to mention — is responsible for the search module.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "thomas_muller", "text": "Is responsible for the search module", "speaker": "user"}]}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "sydney", "label": "Sydney", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "chess", "label": "Chess", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "photography", "label": "Photography", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Personal life

PREVIOUS ASSISTANT: null
USER: Mi partner quiere ir al parque el fin de semana.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "my_partner", "label": "My Partner", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "luca_rossi"}], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "bariloche", "label": "Bariloche", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gardening", "label": "Gardening", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Some project

PREVIOUS ASSISTANT: I've updated the information.
USER: Nos vemos!

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "bariloche", "label": "Bariloche", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "photography", "label": "Photography", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Personal life

PREVIOUS ASSISTANT: null
USER: Mi husband quiere ir al parque el fin de semana.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "my_husband", "label": "My Husband", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "yuki_tanaka"}], "relations": [], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: null

PREVIOUS ASSISTANT: null
USER: I'm working on Beacon, a API service built with Angular and Express. We're using PostgreSQL for the database. The team lead is James O'Brien.

[Assistant] {"topic": {"action": "changed", "label": "Beacon development"}, "entities": [{"id": "beacon", "label": "Beacon", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "angular", "label": "Angular", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "postgresql", "label": "PostgreSQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "beacon", "target": "harbor_systems", "relation": "part_of"}, {"source": "beacon", "target": "angular", "relation": "uses_technology"}, {"source": "beacon", "target": "express", "relation": "uses_technology"}, {"source": "beacon", "target": "postgresql", "relation": "uses_technology"}, {"source": "james_obrien", "target": "harbor_systems", "relation": "works_at"}, {"source": "james_obrien", "target": "beacon", "relation": "maintains"}], "facts": []}


[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with topic classification, entities, relations, and facts. Output valid JSON only, no markdown, no explanation.

[User]
EXISTING NODES:
[{"id": "mendoza", "label": "Mendoza", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "knitting", "label": "Knitting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Personal life

PREVIOUS ASSISTANT: null
USER: My wife is Carlos Mendez, they live in Mendoza too.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Is the user's wife", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "carlos_mendez", "target": "mendoza", "relation": "located_in"}], "facts": []}