[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:

Meeting notes for Beacon sprint planning. Attendees: Lisa Wong, Maria Rodriguez, Carlos Mendez. Discussed migration to MongoDB.

[Assistant] {"topic": {"action": "changed", "label": "Beacon development"}, "entities": [{"id": "beacon", "label": "Beacon", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "beacon_meeting_notes", "label": "Beacon Meeting Notes", "type": "document", "layer": "PERSONAL", "attributes": {"description": "Meeting notes for Beacon sprint planning. Attendees: Lisa Wong, Maria Rodriguez, Carlos Mendez. Disc"}, "facts": [], "existing_id": null}], "relations": [{"source": "beacon", "target": "mongodb", "relation": "uses_technology"}, {"source": "beacon", "target": "fastapi", "relation": "uses_technology"}, {"source": "beacon_meeting_notes", "target": "beacon", "relation": "documented_in"}, {"source": "lisa_wong", "target": "beacon", "relation": "maintains"}, {"source": "maria_rodriguez", "target": "beacon", "relation": "maintains"}, {"source": "carlos_mendez", "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": "echo_app", "label": "Echo", "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": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "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: Echo development

PREVIOUS ASSISTANT: null
USER: The app needs a new feature in the dashboard.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "the_platform", "label": "The Platform", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "echo_app"}], "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": "statistics", "label": "Statistics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_smith", "label": "Professor Smith", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gradient_descent", "label": "Gradient Descent", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "turing_machine", "label": "Turing Machine", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Statistics course

PREVIOUS ASSISTANT: null
USER: Can you explain Turing Machine 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": "computer_networks", "label": "Computer Networks", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_nakamura", "label": "Professor Nakamura", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "attention_mechanism", "label": "Attention Mechanism", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "pagerank", "label": "PageRank", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "backpropagation", "label": "Backpropagation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Computer Networks course

PREVIOUS ASSISTANT: null
USER: Oh yeah, has office hours on mondays.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "prof_nakamura", "text": "Has office hours on Mondays", "speaker": "user"}, {"entity": "prof_nakamura", "text": "Published a paper on backpropagation", "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": "buenos_aires", "label": "Buenos Aires", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "cooking", "label": "Cooking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: I should mention my partner Julia Santos.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Is the user's partner", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "julia_santos", "target": "buenos_aires", "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: I've processed the document. Here's what I found:
USER: Here's a summary of the document I just read:

Meeting notes for Forge sprint planning. Attendees: Omar Hassan. Discussed migration to React.

[Assistant] {"topic": {"action": "changed", "label": "Forge development"}, "entities": [{"id": "forge", "label": "Forge", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "forge_meeting_notes", "label": "Forge Meeting Notes", "type": "document", "layer": "PERSONAL", "attributes": {"description": "Meeting notes for Forge sprint planning. Attendees: Omar Hassan. Discussed migration to React."}, "facts": [], "existing_id": null}], "relations": [{"source": "forge", "target": "react", "relation": "uses_technology"}, {"source": "forge", "target": "fastapi", "relation": "uses_technology"}, {"source": "forge_meeting_notes", "target": "forge", "relation": "documented_in"}, {"source": "omar_hassan", "target": "forge", "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:
[]

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

PREVIOUS ASSISTANT: null
USER: Vivo en Bariloche con mi partner Omar Hassan. Últimamente estoy metido en reading.

[Assistant] {"topic": {"action": "changed", "label": "Personal life"}, "entities": [{"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Is the user's partner", "speaker": "user"}], "existing_id": null}, {"id": "bariloche", "label": "Bariloche", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "reading", "label": "Reading", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "omar_hassan", "target": "bariloche", "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: null
USER: Te cuento del proyecto Compass. Es una aplicación web que estamos armando en Skyline Tech. Stack: Rails + Angular, base de datos en Firebase.

[Assistant] {"topic": {"action": "changed", "label": "Compass development"}, "entities": [{"id": "compass", "label": "Compass", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "angular", "label": "Angular", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "compass", "target": "skyline_tech", "relation": "part_of"}, {"source": "compass", "target": "angular", "relation": "uses_technology"}, {"source": "compass", "target": "rails", "relation": "uses_technology"}, {"source": "compass", "target": "firebase_db", "relation": "uses_technology"}, {"source": "aisha_patel", "target": "skyline_tech", "relation": "works_at"}, {"source": "aisha_patel", "target": "compass", "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": "strato", "label": "Strato", "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": "express", "label": "Express", "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": {}}]

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

PREVIOUS ASSISTANT: Got it, tell me more about Strato.
USER: Carlos Mendez started this week as our new QA engineer. Oh, and we're now using Rust.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "rust_lang", "label": "Rust", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as QA engineer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "strato", "target": "rust_lang", "relation": "uses_technology"}, {"source": "carlos_mendez", "target": "strato", "relation": "maintains"}, {"source": "carlos_mendez", "target": "acme_corp", "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": "prism", "label": "Prism", "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": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "postgresql", "label": "PostgreSQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: What else about Prism?
USER: The payments is the next thing I need to figure out.

[Assistant] {"topic": {"action": "subtopic", "label": "Prism payments"}, "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": "echo_app", "label": "Echo", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at NovaTech"}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "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": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aws", "label": "AWS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "terraform", "label": "Terraform", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rust_lang", "label": "Rust", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "typescript", "label": "TypeScript", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "github_actions", "label": "GitHub Actions", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "kotlin_lang", "label": "Kotlin", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "jenkins", "label": "Jenkins", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Quién es el tech lead de Echo?

[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": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Elena Volkov is joining the team as designer to work on Vertex.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "vertex_app", "label": "Vertex", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as designer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "vertex_app", "target": "blueshift_labs", "relation": "part_of"}, {"source": "elena_volkov", "target": "vertex_app", "relation": "maintains"}, {"source": "elena_volkov", "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": "calculus", "label": "Calculus", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_nakamura", "label": "Professor Nakamura", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "attention_mechanism", "label": "Attention Mechanism", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Calculus course

PREVIOUS ASSISTANT: null
USER: When is the Calculus midterm?

[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: Here's a summary of the paper:
USER: Summary of the paper "A Survey of Backpropagation in Microeconomics":

This paper reviews Backpropagation and its relationship to Eigenvalues. Authors: Professor Williams, Professor Smith. Key finding: reduced complexity.

[Assistant] {"topic": {"action": "changed", "label": "Microeconomics research"}, "entities": [{"id": "a_survey_of_backpropagation_in_microecon", "label": "A Survey of Backpropagation in Microeconomics", "type": "document", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "backpropagation", "label": "Backpropagation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "eigenvalues", "label": "Eigenvalues", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "prof_williams", "label": "Professor Williams", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "prof_smith", "label": "Professor Smith", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "a_survey_of_backpropagation_in_microecon", "target": "backpropagation", "relation": "documented_in"}, {"source": "a_survey_of_backpropagation_in_microecon", "target": "eigenvalues", "relation": "documented_in"}, {"source": "prof_williams", "target": "a_survey_of_backpropagation_in_microecon", "relation": "created_by"}, {"source": "prof_smith", "target": "a_survey_of_backpropagation_in_microecon", "relation": "created_by"}], "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: Here's the chapter summary:
USER: Summary of Star Wars, Chapter 20:

Obi-Wan Kenobi travels to Tatooine where they encounter Emperor Palpatine. A conflict arises when an ancient prophecy is revealed. Obi-Wan Kenobi must decide whether to fight.

[Assistant] {"topic": {"action": "changed", "label": "Star Wars"}, "entities": [{"id": "obi_wan", "label": "Obi-Wan Kenobi", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "palpatine", "label": "Emperor Palpatine", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "leia_organa", "label": "Leia Organa", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "tatooine", "label": "Tatooine", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "chapter_20_conflict", "label": "Chapter 20 Conflict", "type": "event", "layer": "UNIVERSAL", "attributes": {"description": "Conflict at Tatooine", "temporal_marker": "Chapter 20"}, "facts": [], "existing_id": null}], "relations": [{"source": "obi_wan", "target": "chapter_20_conflict", "relation": "participated_in"}, {"source": "palpatine", "target": "chapter_20_conflict", "relation": "participated_in"}, {"source": "chapter_20_conflict", "target": "tatooine", "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": "horizon", "label": "Horizon", "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": "flask", "label": "Flask", "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": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "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": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "docker", "label": "Docker", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: We're not using Firebase 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": "horizon", "target": "spring_boot", "relation": "uses_technology"}], "facts": [{"entity": "horizon", "text": "Migrated from Firebase 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": "compass", "label": "Compass", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Clearpath"}}, {"id": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "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": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "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": "aws", "label": "AWS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "kubernetes", "label": "Kubernetes", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Our product has been getting good feedback.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "our_project", "label": "Our Project", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "compass"}], "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": "gimli", "label": "Gimli", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gandalf", "label": "Gandalf", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "samwise", "label": "Samwise Gamgee", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "boromir", "label": "Boromir", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mordor", "label": "Mordor", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "isengard", "label": "Isengard", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: What would you like to know about Lord of the Rings?
USER: I want to dig deeper into Gandalf's character arc.

[Assistant] {"topic": {"action": "subtopic", "label": "Gandalf's character"}, "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": "cordoba_ar", "label": "Córdoba", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "reading", "label": "Reading", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "running", "label": "Running", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "painting", "label": "Painting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "cooking", "label": "Cooking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Can we focus on my painting goals?

[Assistant] {"topic": {"action": "subtopic", "label": "Painting hobby"}, "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": "leia_organa", "label": "Leia Organa", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "chewbacca", "label": "Chewbacca", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "obi_wan", "label": "Obi-Wan Kenobi", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "luke_skywalker", "label": "Luke Skywalker", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hoth", "label": "Hoth", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: changed (medium confidence — verify)
CURRENT TOPIC: Star Wars

PREVIOUS ASSISTANT: null
USER: OK, I need to study now. I have Data Structures with Professor Williams tomorrow.

[Assistant] {"topic": {"action": "changed", "label": "Data Structures course"}, "entities": [{"id": "data_structures", "label": "Data Structures", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "prof_williams", "label": "Professor Williams", "type": "person", "layer": "PERSONAL", "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": "horizon", "label": "Horizon", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Ironforge"}}, {"id": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "spring_boot", "label": "Spring Boot", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Got it, so Horizon uses Spring Boot and React.
USER: Para que sepas, using version 18.9.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "react", "text": "Using version 18.9", "speaker": "user"}, {"entity": "julia_santos", "text": "Is responsible for the dashboard 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": "apex", "label": "Apex", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Vertex Solutions"}}, {"id": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "postgresql", "label": "PostgreSQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "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": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "kubernetes", "label": "Kubernetes", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Quién es el tech lead de Apex?

[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: Here's the chapter summary:
USER: Summary of Dune, Chapter 3:

Baron Harkonnen travels to Caladan where they encounter Lady Jessica. A conflict arises when a hidden enemy is revealed. Baron Harkonnen must decide whether to negotiate.

[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}, {"id": "chapter_3_conflict", "label": "Chapter 3 Conflict", "type": "event", "layer": "UNIVERSAL", "attributes": {"description": "Conflict at Caladan", "temporal_marker": "Chapter 3"}, "facts": [], "existing_id": null}], "relations": [{"source": "baron_harkonnen", "target": "chapter_3_conflict", "relation": "participated_in"}, {"source": "jessica", "target": "chapter_3_conflict", "relation": "participated_in"}, {"source": "chapter_3_conflict", "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": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "summit", "label": "Summit", "type": "project", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Our company just got new funding.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "our_company", "label": "Our Company", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "ironforge"}], "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": "hermione_granger", "label": "Hermione Granger", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "voldemort", "label": "Lord Voldemort", "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": "ron_weasley", "label": "Ron Weasley", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "snape", "label": "Severus Snape", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "dumbledore", "label": "Albus Dumbledore", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hogsmeade", "label": "Hogsmeade", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "privet_drive", "label": "4 Privet Drive", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "diagon_alley", "label": "Diagon Alley", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: So the hero finally confronted the enemy.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "the_hero", "label": "The Hero", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": "ron_weasley"}], "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": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Acme Corp team

PREVIOUS ASSISTANT: null
USER: Luca Rossi is joining the team as frontend developer to work on Horizon.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "horizon", "label": "Horizon", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as frontend developer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "horizon", "target": "acme_corp", "relation": "part_of"}, {"source": "luca_rossi", "target": "horizon", "relation": "maintains"}, {"source": "luca_rossi", "target": "acme_corp", "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": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "prism", "label": "Prism", "type": "project", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: NovaTech team

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

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "el_cliente", "label": "El Cliente", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "acme_corp"}], "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": "microeconomics", "label": "Microeconomics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_williams", "label": "Professor Williams", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "backpropagation", "label": "Backpropagation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "pagerank", "label": "PageRank", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gradient_descent", "label": "Gradient Descent", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Microeconomics course

PREVIOUS ASSISTANT: null
USER: I need to plan a trip to Tokyo.

[Assistant] {"topic": {"action": "changed", "label": "Travel planning"}, "entities": [{"id": "tokyo", "label": "Tokyo", "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": "flux", "label": "Flux", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Acme Corp"}}, {"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "express", "label": "Express", "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": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "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 keyword match)
CURRENT TOPIC: Flux development

PREVIOUS ASSISTANT: Got it, tell me more about Flux.
USER: We also added GCP to the stack, and Carlos Mendez just joined as backend developer.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "gcp", "label": "GCP", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as backend developer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "flux", "target": "gcp", "relation": "uses_technology"}, {"source": "carlos_mendez", "target": "flux", "relation": "maintains"}, {"source": "carlos_mendez", "target": "acme_corp", "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": "mendoza", "label": "Mendoza", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "swimming", "label": "Swimming", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: I need to plan a trip to London.

[Assistant] {"topic": {"action": "changed", "label": "Travel planning"}, "entities": [{"id": "london", "label": "London", "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": "frodo", "label": "Frodo Baggins", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gandalf", "label": "Gandalf", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gimli", "label": "Gimli", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "samwise", "label": "Samwise Gamgee", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gollum", "label": "Gollum", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sauron", "label": "Sauron", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rivendell", "label": "Rivendell", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "isengard", "label": "Isengard", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mordor", "label": "Mordor", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: The part where Council of Elrond happens is incredible. Sauron, Frodo Baggins, Gollum are all involved. It takes place at Rivendell.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "council_of_elrond", "label": "Council of Elrond", "type": "event", "layer": "UNIVERSAL", "attributes": {"description": "Meeting to decide the fate of the Ring", "temporal_marker": "early in the story"}, "facts": [], "existing_id": null}], "relations": [{"source": "sauron", "target": "council_of_elrond", "relation": "participated_in"}, {"source": "frodo", "target": "council_of_elrond", "relation": "participated_in"}, {"source": "gollum", "target": "council_of_elrond", "relation": "participated_in"}, {"source": "council_of_elrond", "target": "rivendell", "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": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Ironforge team

PREVIOUS ASSISTANT: null
USER: Update: Thomas Müller moved to the QA engineer position.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "thomas_muller", "text": "Now works as QA engineer", "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": "chewbacca", "label": "Chewbacca", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "darth_vader", "label": "Darth Vader", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "luke_skywalker", "label": "Luke Skywalker", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hoth", "label": "Hoth", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Tell me more about Star Wars.
USER: There's also Yoda who appears in the story. They interact with Darth Vader a lot.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "yoda", "label": "Yoda", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "yoda", "target": "darth_vader", "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:
[{"id": "paris", "label": "Paris", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "chess", "label": "Chess", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Back to coding — I'm starting Vertex, using Nuxt.

[Assistant] {"topic": {"action": "changed", "label": "Vertex development"}, "entities": [{"id": "vertex_app", "label": "Vertex", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "vertex_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": "nova", "label": "Nova", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at NovaTech"}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "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": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Oh, I forgot to mention — currently in alpha.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "nova", "text": "Currently in alpha", "speaker": "user"}, {"entity": "nova", "text": "The team has 8 developers", "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: Here's the chapter summary:
USER: Summary of Harry Potter, Chapter 5:

Ron Weasley travels to Hogsmeade where they encounter Albus Dumbledore. A conflict arises when a betrayal is revealed. Ron Weasley must decide whether to negotiate.

[Assistant] {"topic": {"action": "changed", "label": "Harry Potter"}, "entities": [{"id": "ron_weasley", "label": "Ron Weasley", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "dumbledore", "label": "Albus Dumbledore", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "hogsmeade", "label": "Hogsmeade", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "chapter_5_conflict", "label": "Chapter 5 Conflict", "type": "event", "layer": "UNIVERSAL", "attributes": {"description": "Conflict at Hogsmeade", "temporal_marker": "Chapter 5"}, "facts": [], "existing_id": null}], "relations": [{"source": "ron_weasley", "target": "chapter_5_conflict", "relation": "participated_in"}, {"source": "dumbledore", "target": "chapter_5_conflict", "relation": "participated_in"}, {"source": "chapter_5_conflict", "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": "summit", "label": "Summit", "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": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "kubernetes", "label": "Kubernetes", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: We're not using MongoDB anymore, we migrated to Flask.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "summit", "target": "flask", "relation": "uses_technology"}], "facts": [{"entity": "summit", "text": "Migrated from MongoDB to Flask", "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": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "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: Harbor Systems team

PREVIOUS ASSISTANT: null
USER: New project: Horizon. Maria Rodriguez (SRE) is heading it up.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "horizon", "label": "Horizon", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as SRE", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "horizon", "target": "harbor_systems", "relation": "part_of"}, {"source": "maria_rodriguez", "target": "horizon", "relation": "maintains"}, {"source": "maria_rodriguez", "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": "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": "angular", "label": "Angular", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "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": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "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": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "terraform", "label": "Terraform", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Got it, so Compass uses Rails and Angular.
USER: Un update de Compass: Using version 18.8.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "angular", "text": "Using version 18.8", "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": "apex", "label": "Apex", "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": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "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: changed (medium confidence — verify)
CURRENT TOPIC: Apex development

PREVIOUS ASSISTANT: null
USER: Have you heard about React? I'm curious about it.

[Assistant] {"topic": {"action": "changed", "label": "Learning React"}, "entities": [{"id": "react", "label": "React", "type": "technology", "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": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "skyline_tech", "label": "Skyline Tech", "type": "organization", "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": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "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": "zenith", "label": "Zenith", "type": "project", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Harbor Systems team

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

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "el_cliente", "label": "El Cliente", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "skyline_tech"}], "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 Meridian Software"}}, {"id": "meridian_software", "label": "Meridian Software", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "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": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "terraform", "label": "Terraform", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Two days ago, we did a production incident with Aisha Patel and Thomas Müller. Outage in Orbit API module.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "incident", "label": "Production Incident", "type": "event", "layer": "PERSONAL", "attributes": {"description": "Outage in Orbit API module", "temporal_marker": "two days ago"}, "facts": [], "existing_id": null}], "relations": [{"source": "aisha_patel", "target": "incident", "relation": "participated_in"}, {"source": "thomas_muller", "target": "incident", "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": "skyline_tech", "label": "Skyline Tech", "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": "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": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "forge", "label": "Forge", "type": "project", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Skyline Tech team

PREVIOUS ASSISTANT: Tell me more about Skyline Tech.
USER: I should mention that has 187 employees.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "skyline_tech", "text": "Has 187 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": "organic_chemistry", "label": "Organic Chemistry", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_zhang", "label": "Professor Zhang", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "regularization", "label": "Regularization", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "eigenvalues", "label": "Eigenvalues", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Oh yeah, key formula involves matrices.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "regularization", "text": "Key formula involves matrices", "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": "echo_app", "label": "Echo", "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": "spring_boot", "label": "Spring Boot", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Anything else about Echo?
USER: Completely different topic: I started gardening recently.

[Assistant] {"topic": {"action": "changed", "label": "Travel"}, "entities": [{"id": "paris", "label": "Paris", "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": "operating_systems", "label": "Operating Systems", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_kumar", "label": "Professor Kumar", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "big_o_notation", "label": "Big O Notation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Operating Systems course

PREVIOUS ASSISTANT: null
USER: New topic in Operating Systems: Transformer Architecture.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "transformer_arch", "label": "Transformer Architecture", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "transformer_arch", "target": "operating_systems", "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: Here's the chapter summary:
USER: Summary of Harry Potter, Chapter 7:

Ron Weasley travels to 4 Privet Drive where they encounter Draco Malfoy. A conflict arises when a mysterious artifact is revealed. Ron Weasley must decide whether to negotiate.

[Assistant] {"topic": {"action": "changed", "label": "Harry Potter"}, "entities": [{"id": "ron_weasley", "label": "Ron Weasley", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "draco_malfoy", "label": "Draco Malfoy", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "mcgonagall", "label": "Minerva McGonagall", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "harry_potter", "label": "Harry Potter", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "privet_drive", "label": "4 Privet Drive", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "chapter_7_conflict", "label": "Chapter 7 Conflict", "type": "event", "layer": "UNIVERSAL", "attributes": {"description": "Conflict at 4 Privet Drive", "temporal_marker": "Chapter 7"}, "facts": [], "existing_id": null}], "relations": [{"source": "ron_weasley", "target": "chapter_7_conflict", "relation": "participated_in"}, {"source": "draco_malfoy", "target": "chapter_7_conflict", "relation": "participated_in"}, {"source": "chapter_7_conflict", "target": "privet_drive", "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": "helix", "label": "Helix", "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": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "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": "bob_martinez", "label": "Bob Martinez", "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": "terraform", "label": "Terraform", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: En realidad, cambiamos de Nuxt a Next.js en el frontend.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "helix", "target": "nextjs", "relation": "uses_technology"}], "facts": [{"entity": "helix", "text": "Migrated frontend from Nuxt to Next.js", "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": "new_york", "label": "New York", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "woodworking", "label": "Woodworking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

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

[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": "new_york", "label": "New York", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "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": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yoga", "label": "Yoga", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "cycling", "label": "Cycling", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Can we focus on my cycling goals?

[Assistant] {"topic": {"action": "subtopic", "label": "Cycling hobby"}, "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": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gardening", "label": "Gardening", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "swimming", "label": "Swimming", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Can we focus on my swimming goals?

[Assistant] {"topic": {"action": "subtopic", "label": "Swimming hobby"}, "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": "gollum", "label": "Gollum", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sauron", "label": "Sauron", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "isengard", "label": "Isengard", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "minas_tirith", "label": "Minas Tirith", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Lord of the Rings

PREVIOUS ASSISTANT: What else about Lord of the Rings?
USER: I noticed that Gollum — is described as wise.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "gollum", "text": "Is described as wise", "speaker": "user"}, {"entity": "gollum", "text": "Has a special ability: precognition", "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": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "meridian_software", "label": "Meridian Software", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "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": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "compass", "label": "Compass", "type": "project", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: When did we start working with Meridian Software?

[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": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "meridian_software", "label": "Meridian Software", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "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: same (high confidence from keyword match)
CURRENT TOPIC: Pinnacle Digital team

PREVIOUS ASSISTANT: null
USER: We had a client meeting last week with Maria Rodriguez and Elena Volkov and Sarah Johnson. Met with Meridian Software to discuss requirements.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "client_meeting", "label": "Client Meeting", "type": "event", "layer": "PERSONAL", "attributes": {"description": "Met with Meridian Software to discuss requirements", "temporal_marker": "last week"}, "facts": [], "existing_id": null}], "relations": [{"source": "maria_rodriguez", "target": "client_meeting", "relation": "participated_in"}, {"source": "elena_volkov", "target": "client_meeting", "relation": "participated_in"}, {"source": "sarah_johnson", "target": "client_meeting", "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": "vertex_app", "label": "Vertex", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Skyline Tech"}}, {"id": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "spring_boot", "label": "Spring Boot", "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": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Thanks for the context about Vertex.
USER: Quick update on Vertex: The team has 10 developers. Using version 16.3.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "vertex_app", "text": "The team has 10 developers", "speaker": "user"}, {"entity": "nextjs", "text": "Using version 16.3", "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": "buenos_aires", "label": "Buenos Aires", "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": "lisa_wong", "label": "Lisa Wong", "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": "guitar", "label": "Guitar", "type": "concept", "layer": "UNIVERSAL", "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: About the guitar thing — I've been making progress.

[Assistant] {"topic": {"action": "subtopic", "label": "Guitar hobby"}, "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": "paul_atreides", "label": "Paul Atreides", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "jessica", "label": "Lady Jessica", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "leto_atreides", "label": "Duke Leto Atreides", "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": "chani", "label": "Chani", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "duncan_idaho", "label": "Duncan Idaho", "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": {}}]

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

PREVIOUS ASSISTANT: null
USER: The part where the hero reveals the truth was amazing.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "the_hero", "label": "The Hero", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": "chani"}], "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": "compass", "label": "Compass", "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": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "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: Personal life

PREVIOUS ASSISTANT: null
USER: Joya.

[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": "mendoza", "label": "Mendoza", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "hiking", "label": "Hiking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "knitting", "label": "Knitting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: changed (medium confidence — verify)
CURRENT TOPIC: Personal life

PREVIOUS ASSISTANT: null
USER: Back to coding — I'm starting Zenith, using Next.js.

[Assistant] {"topic": {"action": "changed", "label": "Zenith development"}, "entities": [{"id": "zenith", "label": "Zenith", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "zenith", "target": "nextjs", "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": "tokyo", "label": "Tokyo", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "chess", "label": "Chess", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "cooking", "label": "Cooking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
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": "calculus", "label": "Calculus", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_williams", "label": "Professor Williams", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "attention_mechanism", "label": "Attention Mechanism", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "transformer_arch", "label": "Transformer Architecture", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "big_o_notation", "label": "Big O Notation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "backpropagation", "label": "Backpropagation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "eigenvalues", "label": "Eigenvalues", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "microeconomics", "label": "Microeconomics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "machine_learning", "label": "Machine Learning", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: subtopic of Calculus course (medium confidence — verify)
CURRENT TOPIC: Calculus course

PREVIOUS ASSISTANT: null
USER: Let's dive deeper into Attention Mechanism. I'm struggling with it.

[Assistant] {"topic": {"action": "subtopic", "label": "Attention Mechanism"}, "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 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": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Hey, completely off topic — have you read Star Wars? I love Emperor Palpatine.

[Assistant] {"topic": {"action": "changed", "label": "Star Wars"}, "entities": [{"id": "palpatine", "label": "Emperor Palpatine", "type": "person", "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": "tokyo", "label": "Tokyo", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "swimming", "label": "Swimming", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "reading", "label": "Reading", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Oh I forgot, great nightlife.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "tokyo", "text": "Great nightlife", "speaker": "user"}, {"entity": "tokyo", "text": "The commute is 14 minutes", "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": "leto_atreides", "label": "Duke Leto Atreides", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "stilgar", "label": "Stilgar", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "giedi_prime", "label": "Giedi Prime", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "caladan", "label": "Caladan", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: OK, I need to study now. I have Calculus with Professor Kumar tomorrow.

[Assistant] {"topic": {"action": "changed", "label": "Calculus course"}, "entities": [{"id": "calculus", "label": "Calculus", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "prof_kumar", "label": "Professor Kumar", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [], "facts": []}