[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 Skyline Tech"}}, {"id": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gcp", "label": "GCP", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "terraform", "label": "Terraform", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Un update de Summit: Currently in alpha. Has 52 tables.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "summit", "text": "Currently in alpha", "speaker": "user"}, {"entity": "redis", "text": "Has 52 tables", "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": "zenith", "label": "Zenith", "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": "spring_boot", "label": "Spring Boot", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Here's what I found about that.
USER: Right.

[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": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yoga", "label": "Yoga", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: We had graduation el sábado pasado. Luca Rossi were there. Ceremony at the local university.

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


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

[User]
EXISTING NODES:
[{"id": "orbit", "label": "Orbit", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Vertex Solutions"}}, {"id": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "express", "label": "Express", "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": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Got it, tell me more about Orbit.
USER: We brought in Lisa Wong as frontend developer and started using Swift for the project.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "swift_lang", "label": "Swift", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as frontend developer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "orbit", "target": "swift_lang", "relation": "uses_technology"}, {"source": "lisa_wong", "target": "orbit", "relation": "maintains"}, {"source": "lisa_wong", "target": "vertex_solutions", "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:
[]

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

PREVIOUS ASSISTANT: null
USER: This semester I'm enrolled in Data Structures. Professor Nakamura is teaching it. So far we've covered Big O Notation.

[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_nakamura", "label": "Professor Nakamura", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "big_o_notation", "label": "Big O Notation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "big_o_notation", "target": "data_structures", "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": "draco_malfoy", "label": "Draco Malfoy", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "harry_potter", "label": "Harry Potter", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ron_weasley", "label": "Ron Weasley", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "snape", "label": "Severus Snape", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ministry_of_magic", "label": "Ministry of Magic", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "privet_drive", "label": "4 Privet Drive", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hogsmeade", "label": "Hogsmeade", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: subtopic of Harry Potter (medium confidence — verify)
CURRENT TOPIC: Harry Potter

PREVIOUS ASSISTANT: What would you like to know about Harry Potter?
USER: I want to dig deeper into Severus Snape's character arc.

[Assistant] {"topic": {"action": "subtopic", "label": "Severus Snape'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": "toronto", "label": "Toronto", "type": "place", "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": "swimming", "label": "Swimming", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Actually, we moved to Berlin last month.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "toronto", "text": "Relocated to Berlin", "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": "zenith", "label": "Zenith", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Clearpath"}}, {"id": "clearpath", "label": "Clearpath", "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": "alice_chen", "label": "Alice Chen", "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": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gcp", "label": "GCP", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "docker", "label": "Docker", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "kubernetes", "label": "Kubernetes", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "go_lang", "label": "Go", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "kotlin_lang", "label": "Kotlin", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bootstrap", "label": "Bootstrap", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "python_lang", "label": "Python", "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": {}}]

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

PREVIOUS ASSISTANT: null
USER: Should we consider PostgreSQL instead of MongoDB?

[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": "buenos_aires", "label": "Buenos Aires", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "woodworking", "label": "Woodworking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

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

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


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

[User]
EXISTING NODES:
[]

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

PREVIOUS ASSISTANT: Here's the chapter summary:
USER: Summary of Lord of the Rings, Chapter 4:

Boromir travels to The Shire where they encounter Samwise Gamgee. A conflict arises when a hidden enemy is revealed. Boromir must decide whether to sacrifice.

[Assistant] {"topic": {"action": "changed", "label": "Lord of the Rings"}, "entities": [{"id": "boromir", "label": "Boromir", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "samwise", "label": "Samwise Gamgee", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "aragorn", "label": "Aragorn", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "the_shire", "label": "The Shire", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "chapter_4_conflict", "label": "Chapter 4 Conflict", "type": "event", "layer": "UNIVERSAL", "attributes": {"description": "Conflict at The Shire", "temporal_marker": "Chapter 4"}, "facts": [], "existing_id": null}], "relations": [{"source": "boromir", "target": "chapter_4_conflict", "relation": "participated_in"}, {"source": "samwise", "target": "chapter_4_conflict", "relation": "participated_in"}, {"source": "chapter_4_conflict", "target": "the_shire", "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": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "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": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "strato", "label": "Strato", "type": "project", "layer": "PERSONAL", "attributes": {}}]

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

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

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


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

[User]
EXISTING NODES:
[{"id": "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": "transformer_arch", "label": "Transformer Architecture", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "big_o_notation", "label": "Big O Notation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "overfitting", "label": "Overfitting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "calculus", "label": "Calculus", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Computer Networks course

PREVIOUS ASSISTANT: null
USER: I should add that uses python for assignments.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "computer_networks", "text": "Uses Python for assignments", "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": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: What's the budget for the Acme Corp project?

[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": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

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

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


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

[User]
EXISTING NODES:
[{"id": "onyx", "label": "Onyx", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Skyline Tech"}}, {"id": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: How does the search module work in Onyx?

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


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

[User]
EXISTING NODES:
[{"id": "catalyst", "label": "Catalyst", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Acme Corp"}}, {"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "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": "thomas_muller", "label": "Thomas Müller", "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 embedding similarity)
CURRENT TOPIC: Catalyst development

PREVIOUS ASSISTANT: Got it, tell me more about Catalyst.
USER: We brought in Bob Martinez as CTO and started using AWS for the project.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "aws", "label": "AWS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as CTO", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "catalyst", "target": "aws", "relation": "uses_technology"}, {"source": "bob_martinez", "target": "catalyst", "relation": "maintains"}, {"source": "bob_martinez", "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": "legolas", "label": "Legolas", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gollum", "label": "Gollum", "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: changed (medium confidence — verify)
CURRENT TOPIC: Lord of the Rings

PREVIOUS ASSISTANT: null
USER: Changing topics — Data Structures exam is coming up. Professor Zhang's class.

[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_zhang", "label": "Professor Zhang", "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:
[]

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

PREVIOUS ASSISTANT: Here's the chapter summary:
USER: Summary of Dune, Chapter 14:

Chani travels to Giedi Prime where they encounter Baron Harkonnen. A conflict arises when an ancient prophecy is revealed. Chani must decide whether to flee.

[Assistant] {"topic": {"action": "changed", "label": "Dune"}, "entities": [{"id": "chani", "label": "Chani", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"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": "giedi_prime", "label": "Giedi Prime", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "chapter_14_conflict", "label": "Chapter 14 Conflict", "type": "event", "layer": "UNIVERSAL", "attributes": {"description": "Conflict at Giedi Prime", "temporal_marker": "Chapter 14"}, "facts": [], "existing_id": null}], "relations": [{"source": "chani", "target": "chapter_14_conflict", "relation": "participated_in"}, {"source": "baron_harkonnen", "target": "chapter_14_conflict", "relation": "participated_in"}, {"source": "chapter_14_conflict", "target": "giedi_prime", "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": "mexico_city", "label": "Mexico City", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "reading", "label": "Reading", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "chess", "label": "Chess", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: We had birthday party last Saturday. Bob Martinez and Nina Petrov were there. Celebrated at a restaurant in Mexico City.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "birthday_party", "label": "Birthday Party", "type": "event", "layer": "PERSONAL", "attributes": {"description": "Celebrated at a restaurant in Mexico City", "temporal_marker": "last Saturday"}, "facts": [], "existing_id": null}], "relations": [{"source": "bob_martinez", "target": "birthday_party", "relation": "participated_in"}, {"source": "nina_petrov", "target": "birthday_party", "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": "dumbledore", "label": "Albus Dumbledore", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hagrid", "label": "Rubeus Hagrid", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "draco_malfoy", "label": "Draco Malfoy", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "harry_potter", "label": "Harry Potter", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "privet_drive", "label": "4 Privet Drive", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hogsmeade", "label": "Hogsmeade", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: What else about Harry Potter?
USER: So in the book, motivation is to protect friends.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "hagrid", "text": "Motivation is to protect friends", "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": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: New project: Compass. Yuki Tanaka (CTO) is heading it up.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "compass", "label": "Compass", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as CTO", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "compass", "target": "harbor_systems", "relation": "part_of"}, {"source": "yuki_tanaka", "target": "compass", "relation": "maintains"}, {"source": "yuki_tanaka", "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": "mendoza", "label": "Mendoza", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "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: Also, the commute is 22 minutes.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "mendoza", "text": "The commute is 22 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": "zenith", "label": "Zenith", "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": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aws", "label": "AWS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "github_actions", "label": "GitHub Actions", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Quick update on Zenith: Has 448 tables. Has 62069 monthly active users.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "mysql", "text": "Has 448 tables", "speaker": "user"}, {"entity": "zenith", "text": "Has 62069 monthly active users", "speaker": "user"}]}


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

[User]
EXISTING NODES:
[{"id": "data_structures", "label": "Data Structures", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_zhang", "label": "Professor Zhang", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "backpropagation", "label": "Backpropagation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "attention_mechanism", "label": "Attention Mechanism", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "overfitting", "label": "Overfitting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Data Structures course

PREVIOUS ASSISTANT: null
USER: New topic in Data Structures: 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": "data_structures", "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": "helix", "label": "Helix", "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": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gcp", "label": "GCP", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "github_actions", "label": "GitHub Actions", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Got it, so Helix uses Rails and Next.js.
USER: Some updates: Launched in February 2026. Has 399 tables. Has 172 employees.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "helix", "text": "Launched in February 2026", "speaker": "user"}, {"entity": "redis", "text": "Has 399 tables", "speaker": "user"}, {"entity": "vertex_solutions", "text": "Has 172 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": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Tell me more about Clearpath.
USER: I should mention that got promoted to sre.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "yuki_tanaka", "text": "Got promoted to SRE", "speaker": "user"}, {"entity": "harbor_systems", "text": "Contract is worth $191k", "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": "atlas", "label": "Atlas", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Clearpath"}}, {"id": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "angular", "label": "Angular", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

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

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "our_project", "label": "Our Project", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "atlas"}], "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": "nova", "label": "Nova", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Skyline Tech"}}, {"id": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "jenkins", "label": "Jenkins", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Got it, so Nova uses Rails and Vue.
USER: Quick update on Nova: Is responsible for the search module.

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


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

[User]
EXISTING NODES:
[]

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 Transformer Architecture in Linear Algebra":

This paper reviews Transformer Architecture and its relationship to Gradient Descent. Authors: Professor Kumar, Professor Nakamura. Key finding: improved accuracy by 15%.

[Assistant] {"topic": {"action": "changed", "label": "Linear Algebra research"}, "entities": [{"id": "a_survey_of_transformer_architecture_in_", "label": "A Survey of Transformer Architecture in Linear Algebra", "type": "document", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "transformer_arch", "label": "Transformer Architecture", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "gradient_descent", "label": "Gradient Descent", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "backpropagation", "label": "Backpropagation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "prof_kumar", "label": "Professor Kumar", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "prof_nakamura", "label": "Professor Nakamura", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "a_survey_of_transformer_architecture_in_", "target": "transformer_arch", "relation": "documented_in"}, {"source": "a_survey_of_transformer_architecture_in_", "target": "gradient_descent", "relation": "documented_in"}, {"source": "a_survey_of_transformer_architecture_in_", "target": "backpropagation", "relation": "documented_in"}, {"source": "prof_kumar", "target": "a_survey_of_transformer_architecture_in_", "relation": "created_by"}, {"source": "prof_nakamura", "target": "a_survey_of_transformer_architecture_in_", "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:
[{"id": "horizon", "label": "Horizon", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Vertex Solutions"}}, {"id": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "postgresql", "label": "PostgreSQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Anything else about Horizon?
USER: Switching gears — I went to Mexico City last weekend. Amazing trip.

[Assistant] {"topic": {"action": "changed", "label": "Travel"}, "entities": [{"id": "mexico_city", "label": "Mexico City", "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": "barcelona", "label": "Barcelona", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "cooking", "label": "Cooking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "yoga", "label": "Yoga", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Different topic: Catalyst. We're building it with Flask.

[Assistant] {"topic": {"action": "changed", "label": "Catalyst development"}, "entities": [{"id": "catalyst", "label": "Catalyst", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "catalyst", "target": "flask", "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": "helix", "label": "Helix", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at BlueShift Labs"}}, {"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "angular", "label": "Angular", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Cómo funciona el módulo de búsqueda en Helix?

[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": "harry_potter", "label": "Harry Potter", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ron_weasley", "label": "Ron Weasley", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hagrid", "label": "Rubeus Hagrid", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "snape", "label": "Severus Snape", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "voldemort", "label": "Lord Voldemort", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sirius_black", "label": "Sirius Black", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hermione_granger", "label": "Hermione Granger", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hogsmeade", "label": "Hogsmeade", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ministry_of_magic", "label": "Ministry of Magic", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hogwarts", "label": "Hogwarts", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Let's talk about Sorting Ceremony — new students sorted into houses. Key characters: Hermione Granger, Severus Snape, Ron Weasley.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "sorting_ceremony", "label": "Sorting Ceremony", "type": "event", "layer": "UNIVERSAL", "attributes": {"description": "New students sorted into houses", "temporal_marker": "early in the story"}, "facts": [], "existing_id": null}], "relations": [{"source": "hermione_granger", "target": "sorting_ceremony", "relation": "participated_in"}, {"source": "snape", "target": "sorting_ceremony", "relation": "participated_in"}, {"source": "ron_weasley", "target": "sorting_ceremony", "relation": "participated_in"}, {"source": "sorting_ceremony", "target": "ministry_of_magic", "relation": "located_in"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "summit", "label": "Summit", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Pinnacle Digital"}}, {"id": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nextjs", "label": "Next.js", "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": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aws", "label": "AWS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "github_actions", "label": "GitHub Actions", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: What else about Summit?
USER: Now about the API design specifically — how should we handle it?

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


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

[User]
EXISTING NODES:
[{"id": "quantum_physics", "label": "Quantum Physics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_kumar", "label": "Professor Kumar", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "overfitting", "label": "Overfitting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "attention_mechanism", "label": "Attention Mechanism", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "turing_machine", "label": "Turing Machine", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "linear_algebra", "label": "Linear Algebra", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: I need to understand Overfitting better for the exam.

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


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

[User]
EXISTING NODES:
[]

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

PREVIOUS ASSISTANT: null
USER: Te pongo en contexto. Estoy en Acme Corp, trabajamos con BlueShift Labs. Marco Silva es backend developer y Luca Rossi es DevOps engineer.

[Assistant] {"topic": {"action": "changed", "label": "Acme Corp team"}, "entities": [{"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as backend developer", "speaker": "user"}], "existing_id": null}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as DevOps engineer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "acme_corp", "target": "blueshift_labs", "relation": "serves"}, {"source": "marco_silva", "target": "acme_corp", "relation": "works_at"}, {"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": "beacon", "label": "Beacon", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Skyline Tech"}}, {"id": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "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: Beacon development

PREVIOUS ASSISTANT: null
USER: Random question: what do you think about Paul Atreides in Dune?

[Assistant] {"topic": {"action": "changed", "label": "Dune"}, "entities": [{"id": "paul_atreides", "label": "Paul Atreides", "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": "aragorn", "label": "Aragorn", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "frodo", "label": "Frodo Baggins", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gollum", "label": "Gollum", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gimli", "label": "Gimli", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gandalf", "label": "Gandalf", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "legolas", "label": "Legolas", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "isengard", "label": "Isengard", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rivendell", "label": "Rivendell", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "minas_tirith", "label": "Minas Tirith", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Let's talk about Council of Elrond — meeting to decide the fate of the ring. Key characters: Frodo Baggins, Gollum.

[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": "in the middle of the book"}, "facts": [], "existing_id": null}], "relations": [{"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": "catalyst", "label": "Catalyst", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Vertex Solutions"}}, {"id": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vue", "label": "Vue", "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": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: El lunes pasado, we did a planning meeting with David Kim and Priya Sharma. Planned the next sprint for Catalyst.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "planning_meeting", "label": "Planning Meeting", "type": "event", "layer": "PERSONAL", "attributes": {"description": "Planned the next sprint for Catalyst", "temporal_marker": "el lunes pasado"}, "facts": [], "existing_id": null}], "relations": [{"source": "david_kim", "target": "planning_meeting", "relation": "participated_in"}, {"source": "priya_sharma", "target": "planning_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": "paris", "label": "Paris", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "chess", "label": "Chess", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: We had birthday party last month. Sarah Johnson were there. Celebrated at a restaurant in Paris.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "birthday_party", "label": "Birthday Party", "type": "event", "layer": "PERSONAL", "attributes": {"description": "Celebrated at a restaurant in Paris", "temporal_marker": "last month"}, "facts": [], "existing_id": null}], "relations": [{"source": "sarah_johnson", "target": "birthday_party", "relation": "participated_in"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "forge", "label": "Forge", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at NovaTech"}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "postgresql", "label": "PostgreSQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: same (high confidence from embedding similarity)
CURRENT TOPIC: Forge 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": "forge"}], "relations": [], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "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": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "helix", "label": "Helix", "type": "project", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Tell me more about Harbor Systems.
USER: Oh, contract is worth $168k.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "pinnacle_digital", "text": "Contract is worth $168k", "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": "gandalf", "label": "Gandalf", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gollum", "label": "Gollum", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "frodo", "label": "Frodo Baggins", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "legolas", "label": "Legolas", "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": "gimli", "label": "Gimli", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "the_shire", "label": "The Shire", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mordor", "label": "Mordor", "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: null
USER: In Lord of the Rings, the one ring is destroyed in mount doom. Legolas and Frodo Baggins participate. Happens at Mordor, during the war.

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


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

[User]
EXISTING NODES:
[{"id": "sydney", "label": "Sydney", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "photography", "label": "Photography", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

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

[Assistant] {"topic": {"action": "changed", "label": "Flux development"}, "entities": [{"id": "flux", "label": "Flux", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "flux", "target": "vue", "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:
[]

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

PREVIOUS ASSISTANT: null
USER: Te pongo en contexto. Estoy en NovaTech, trabajamos con Harbor Systems. Omar Hassan es DevOps engineer y Carlos Mendez es CTO.

[Assistant] {"topic": {"action": "changed", "label": "NovaTech team"}, "entities": [{"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as DevOps engineer", "speaker": "user"}], "existing_id": null}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as CTO", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "novatech", "target": "harbor_systems", "relation": "serves"}, {"source": "omar_hassan", "target": "novatech", "relation": "works_at"}, {"source": "carlos_mendez", "target": "novatech", "relation": "works_at"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "boromir", "label": "Boromir", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gollum", "label": "Gollum", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "frodo", "label": "Frodo Baggins", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sauron", "label": "Sauron", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "isengard", "label": "Isengard", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mordor", "label": "Mordor", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rivendell", "label": "Rivendell", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "the_shire", "label": "The Shire", "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: So in the book, motivation is to save the world.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "sauron", "text": "Motivation is to save the world", "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": "microeconomics", "label": "Microeconomics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_nakamura", "label": "Professor Nakamura", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "regularization", "label": "Regularization", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

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

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "regularization", "text": "Key formula involves derivatives", "speaker": "user"}, {"entity": "prof_nakamura", "text": "Has office hours on Mondays", "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": "mexico_city", "label": "Mexico City", "type": "place", "layer": "UNIVERSAL", "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": "chess", "label": "Chess", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Actually, we moved to Barcelona last month.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "mexico_city", "text": "Relocated to Barcelona", "speaker": "user"}]}


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

[User]
EXISTING NODES:
[{"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "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: BlueShift Labs team

PREVIOUS ASSISTANT: Tell me more about BlueShift Labs.
USER: Te cuento que revenue grew 19% last quarter.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "blueshift_labs", "text": "Revenue grew 19% last quarter", "speaker": "user"}, {"entity": "blueshift_labs", "text": "Has 433 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": "mendoza", "label": "Mendoza", "type": "place", "layer": "UNIVERSAL", "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": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "swimming", "label": "Swimming", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "knitting", "label": "Knitting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "cycling", "label": "Cycling", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Oh I forgot, the commute is 15 minutes.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "mendoza", "text": "The commute is 15 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:
[]

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

PREVIOUS ASSISTANT: null
USER: Let's talk about Harry Potter. I'm really into Hermione Granger and Ron Weasley right now. The setting in Hogwarts is amazing.

[Assistant] {"topic": {"action": "changed", "label": "Harry Potter"}, "entities": [{"id": "hermione_granger", "label": "Hermione Granger", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "ron_weasley", "label": "Ron Weasley", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "mcgonagall", "label": "Minerva McGonagall", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "snape", "label": "Severus Snape", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "hogwarts", "label": "Hogwarts", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "hermione_granger", "target": "hogwarts", "relation": "located_in"}, {"source": "ron_weasley", "target": "hogwarts", "relation": "located_in"}, {"source": "mcgonagall", "target": "hogwarts", "relation": "located_in"}, {"source": "snape", "target": "hogwarts", "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": "computer_networks", "label": "Computer Networks", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_zhang", "label": "Professor Zhang", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "transformer_arch", "label": "Transformer Architecture", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "regularization", "label": "Regularization", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gradient_descent", "label": "Gradient Descent", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "big_o_notation", "label": "Big O Notation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "quantum_physics", "label": "Quantum Physics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: I need to understand Transformer Architecture better for the exam.

[Assistant] {"topic": {"action": "subtopic", "label": "Transformer Architecture"}, "entities": [], "relations": [], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "forge", "label": "Forge", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Harbor Systems"}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "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: unresolved — classify the topic yourself
CURRENT TOPIC: Forge development

PREVIOUS ASSISTANT: null
USER: En realidad, cambiamos de Svelte a MySQL en el frontend.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "forge", "target": "mysql", "relation": "uses_technology"}], "facts": [{"entity": "forge", "text": "Migrated frontend from Svelte to MySQL", "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": "operating_systems", "label": "Operating Systems", "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": "regularization", "label": "Regularization", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "big_o_notation", "label": "Big O Notation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "turing_machine", "label": "Turing Machine", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "calculus", "label": "Calculus", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "machine_learning", "label": "Machine Learning", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: When is the Operating Systems 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:
[{"id": "mexico_city", "label": "Mexico City", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "running", "label": "Running", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Yep.

[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": "apex", "label": "Apex", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at NovaTech"}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "angular", "label": "Angular", "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": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aws", "label": "AWS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "terraform", "label": "Terraform", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "github_actions", "label": "GitHub Actions", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "swift_lang", "label": "Swift", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "typescript", "label": "TypeScript", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Qué framework usamos en el frontend 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:
[{"id": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "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": "beacon", "label": "Beacon", "type": "project", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Tell me more about Vertex Solutions.
USER: I should mention that is leading the q3 initiative.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "yuki_tanaka", "text": "Is leading the Q3 initiative", "speaker": "user"}, {"entity": "blueshift_labs", "text": "Contract is worth $242k", "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": "samwise", "label": "Samwise Gamgee", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gollum", "label": "Gollum", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gimli", "label": "Gimli", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "legolas", "label": "Legolas", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "boromir", "label": "Boromir", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sauron", "label": "Sauron", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gandalf", "label": "Gandalf", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rivendell", "label": "Rivendell", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "isengard", "label": "Isengard", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "minas_tirith", "label": "Minas Tirith", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mordor", "label": "Mordor", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: I think the hero made a mistake in that scene.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "the_hero", "label": "The Hero", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": "legolas"}], "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": "beacon", "label": "Beacon", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Ironforge"}}, {"id": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Moving on from work — I just started reading Star Wars.

[Assistant] {"topic": {"action": "changed", "label": "Star Wars"}, "entities": [{"id": "yoda", "label": "Yoda", "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": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "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": "atlas", "label": "Atlas", "type": "project", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: El lead se toma vacaciones.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "the_team_lead", "label": "The Team Lead", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "julia_santos"}], "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": "aragorn", "label": "Aragorn", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "boromir", "label": "Boromir", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sauron", "label": "Sauron", "type": "person", "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: Tell me more about Lord of the Rings.
USER: I just got to the part where Samwise Gamgee shows up. Seems connected to Boromir.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "samwise", "label": "Samwise Gamgee", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "samwise", "target": "boromir", "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": "stilgar", "label": "Stilgar", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "duncan_idaho", "label": "Duncan Idaho", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "paul_atreides", "label": "Paul Atreides", "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 keyword match)
CURRENT TOPIC: Dune

PREVIOUS ASSISTANT: What else about Dune?
USER: In Dune, motivation is to save the world.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "duncan_idaho", "text": "Motivation is to save the world", "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": "quantum_physics", "label": "Quantum Physics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_garcia", "label": "Professor García", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "turing_machine", "label": "Turing Machine", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "regularization", "label": "Regularization", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "attention_mechanism", "label": "Attention Mechanism", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "pagerank", "label": "PageRank", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "overfitting", "label": "Overfitting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "data_structures", "label": "Data Structures", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "operating_systems", "label": "Operating Systems", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: subtopic of Quantum Physics course (medium confidence — verify)
CURRENT TOPIC: Quantum Physics 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": "darth_vader", "label": "Darth Vader", "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": "leia_organa", "label": "Leia Organa", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "death_star", "label": "Death Star", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "endor", "label": "Endor", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "tatooine", "label": "Tatooine", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hoth", "label": "Hoth", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Let's talk about Jedi Training — luke trains with yoda on dagobah. Key characters: Darth Vader, Obi-Wan Kenobi.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "jedi_training", "label": "Jedi Training", "type": "event", "layer": "UNIVERSAL", "attributes": {"description": "Luke trains with Yoda on Dagobah", "temporal_marker": "in the middle of the book"}, "facts": [], "existing_id": null}], "relations": [{"source": "darth_vader", "target": "jedi_training", "relation": "participated_in"}, {"source": "obi_wan", "target": "jedi_training", "relation": "participated_in"}, {"source": "jedi_training", "target": "death_star", "relation": "located_in"}], "facts": []}