[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": "gandalf", "label": "Gandalf", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gollum", "label": "Gollum", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gimli", "label": "Gimli", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "boromir", "label": "Boromir", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "legolas", "label": "Legolas", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mordor", "label": "Mordor", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "the_shire", "label": "The Shire", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rivendell", "label": "Rivendell", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "isengard", "label": "Isengard", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: subtopic of Lord of the Rings (medium confidence — verify)
CURRENT TOPIC: Lord of the Rings

PREVIOUS ASSISTANT: What would you like to know about Lord of the Rings?
USER: Can we analyze Gimli in more detail?

[Assistant] {"topic": {"action": "subtopic", "label": "Gimli'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": "onyx", "label": "Onyx", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Vertex Solutions"}}, {"id": "vertex_solutions", "label": "Vertex Solutions", "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": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "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": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gcp", "label": "GCP", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "kubernetes", "label": "Kubernetes", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

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

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


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

[User]
EXISTING NODES:
[{"id": "mcgonagall", "label": "Minerva McGonagall", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hermione_granger", "label": "Hermione Granger", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "draco_malfoy", "label": "Draco Malfoy", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sirius_black", "label": "Sirius Black", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ministry_of_magic", "label": "Ministry of Magic", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

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

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


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

[User]
EXISTING NODES:
[{"id": "compass", "label": "Compass", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Harbor Systems"}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "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": {}}]

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

PREVIOUS ASSISTANT: null
USER: Actually, we switched from Vue to Nuxt for the frontend.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "compass", "target": "nuxt", "relation": "uses_technology"}], "facts": [{"entity": "compass", "text": "Migrated frontend from Vue to Nuxt", "speaker": "user"}]}


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

[User]
EXISTING NODES:
[]

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

PREVIOUS ASSISTANT: null
USER: I'm taking Microeconomics this semester with Professor Zhang. We just started covering Gradient Descent.

[Assistant] {"topic": {"action": "changed", "label": "Microeconomics course"}, "entities": [{"id": "microeconomics", "label": "Microeconomics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "prof_zhang", "label": "Professor Zhang", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "gradient_descent", "label": "Gradient Descent", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "gradient_descent", "target": "microeconomics", "relation": "part_of"}], "facts": []}


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

[User]
EXISTING NODES:
[]

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

PREVIOUS ASSISTANT: null
USER: Let me introduce myself. I'm from Berlin, my partner Sarah Johnson and I live there. I'm really into gardening.

[Assistant] {"topic": {"action": "changed", "label": "Personal life"}, "entities": [{"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Is the user's partner", "speaker": "user"}], "existing_id": null}, {"id": "berlin", "label": "Berlin", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "gardening", "label": "Gardening", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "sarah_johnson", "target": "berlin", "relation": "located_in"}], "facts": []}


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

[User]
EXISTING NODES:
[]

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

PREVIOUS ASSISTANT: I've updated the information.
USER: Listo.

[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": "flux", "label": "Flux", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at NovaTech"}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

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

[Assistant] {"topic": {"action": "subtopic", "label": "Flux testing"}, "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": "chewbacca", "label": "Chewbacca", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "leia_organa", "label": "Leia Organa", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "darth_vader", "label": "Darth Vader", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "luke_skywalker", "label": "Luke Skywalker", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "obi_wan", "label": "Obi-Wan Kenobi", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "yoda", "label": "Yoda", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "tatooine", "label": "Tatooine", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "coruscant", "label": "Coruscant", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "endor", "label": "Endor", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "death_star", "label": "Death Star", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Star Wars

PREVIOUS ASSISTANT: What would you like to know about Star Wars?
USER: Can we analyze Darth Vader in more detail?

[Assistant] {"topic": {"action": "subtopic", "label": "Darth Vader'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": "nexus", "label": "Nexus", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Ironforge"}}, {"id": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aws", "label": "AWS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "docker", "label": "Docker", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Correction: Julia Santos left the company. Thomas Müller replaced them.

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


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

[User]
EXISTING NODES:
[{"id": "zenith", "label": "Zenith", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Pinnacle Digital"}}, {"id": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "angular", "label": "Angular", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "kubernetes", "label": "Kubernetes", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Actually, we switched from Angular to React for the frontend.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "zenith", "target": "react", "relation": "uses_technology"}], "facts": [{"entity": "zenith", "text": "Migrated frontend from Angular to React", "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": "leia_organa", "label": "Leia Organa", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "chewbacca", "label": "Chewbacca", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "palpatine", "label": "Emperor Palpatine", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "han_solo", "label": "Han Solo", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "darth_vader", "label": "Darth Vader", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "endor", "label": "Endor", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "coruscant", "label": "Coruscant", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Star Wars

PREVIOUS ASSISTANT: What would you like to know about Star Wars?
USER: Can we analyze Han Solo in more detail?

[Assistant] {"topic": {"action": "subtopic", "label": "Han Solo'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": "machine_learning", "label": "Machine Learning", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_smith", "label": "Professor Smith", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "big_o_notation", "label": "Big O Notation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: same (high confidence from keyword match)
CURRENT TOPIC: Machine Learning course

PREVIOUS ASSISTANT: null
USER: New topic in Machine Learning: Eigenvalues.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "eigenvalues", "label": "Eigenvalues", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "eigenvalues", "target": "machine_learning", "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": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "skyline_tech", "label": "Skyline Tech", "type": "organization", "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": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "echo_app", "label": "Echo", "type": "project", "layer": "PERSONAL", "attributes": {}}]

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

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

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


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

[User]
EXISTING NODES:
[{"id": "obi_wan", "label": "Obi-Wan Kenobi", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "luke_skywalker", "label": "Luke Skywalker", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "yoda", "label": "Yoda", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "endor", "label": "Endor", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Star Wars

PREVIOUS ASSISTANT: Tell me more about Star Wars.
USER: New character: Leia Organa. They seem to have a history with Yoda.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "leia_organa", "label": "Leia Organa", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "leia_organa", "target": "yoda", "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": "frodo", "label": "Frodo Baggins", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "legolas", "label": "Legolas", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sauron", "label": "Sauron", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "samwise", "label": "Samwise Gamgee", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "minas_tirith", "label": "Minas Tirith", "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": {}}, {"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: What's the significance of Minas Tirith?

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


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

[User]
EXISTING NODES:
[{"id": "toronto", "label": "Toronto", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "painting", "label": "Painting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "knitting", "label": "Knitting", "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 Bariloche last month.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "toronto", "text": "Relocated to Bariloche", "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": "horizon", "label": "Horizon", "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": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Got it, tell me more about Horizon.
USER: We brought in Sarah Johnson as full-stack developer and started using Azure for the project.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as full-stack developer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "horizon", "target": "azure", "relation": "uses_technology"}, {"source": "sarah_johnson", "target": "horizon", "relation": "maintains"}, {"source": "sarah_johnson", "target": "skyline_tech", "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": "meridian_software", "label": "Meridian Software", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

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

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


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

[User]
EXISTING NODES:
[{"id": "prism", "label": "Prism", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at 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": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aws", "label": "AWS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "docker", "label": "Docker", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bootstrap", "label": "Bootstrap", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "jenkins", "label": "Jenkins", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "python_lang", "label": "Python", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "java_lang", "label": "Java", "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": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Who's the tech lead on Prism?

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


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

[User]
EXISTING NODES:
[{"id": "pulse", "label": "Pulse", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Clearpath"}}, {"id": "clearpath", "label": "Clearpath", "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": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gcp", "label": "GCP", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "terraform", "label": "Terraform", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

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

[Assistant] {"topic": {"action": "subtopic", "label": "Pulse notifications"}, "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": "orbit", "label": "Orbit", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at NovaTech"}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "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": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "azure", "label": "Azure", "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: Orbit development

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

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "the_app", "label": "The App", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "orbit"}], "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": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "swimming", "label": "Swimming", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "running", "label": "Running", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "woodworking", "label": "Woodworking", "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, birthday is in march.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "raj_kapoor", "text": "Birthday is in March", "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": "statistics", "label": "Statistics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_nakamura", "label": "Professor Nakamura", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "eigenvalues", "label": "Eigenvalues", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "big_o_notation", "label": "Big O Notation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Professor Nakamura introduced PageRank today. It's connected to what we studied before.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "pagerank", "label": "PageRank", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "pagerank", "target": "statistics", "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": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "meridian_software", "label": "Meridian Software", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Correction: Priya Sharma is now SRE, not what I said before.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "priya_sharma", "text": "Now works as SRE", "speaker": "user"}]}


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

[User]
EXISTING NODES:
[{"id": "organic_chemistry", "label": "Organic Chemistry", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_nakamura", "label": "Professor Nakamura", "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": "eigenvalues", "label": "Eigenvalues", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "transformer_arch", "label": "Transformer Architecture", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "overfitting", "label": "Overfitting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "statistics", "label": "Statistics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "machine_learning", "label": "Machine Learning", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Organic Chemistry course

PREVIOUS ASSISTANT: null
USER: When is the Organic Chemistry 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": "sydney", "label": "Sydney", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "reading", "label": "Reading", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "cycling", "label": "Cycling", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: My partner is David Kim, they live in Sydney too.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Is the user's partner", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "david_kim", "target": "sydney", "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 Clearpath"}}, {"id": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "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": {}}]

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

PREVIOUS ASSISTANT: I see, Catalyst is built with React.
USER: Quick update on Catalyst: Using version 15.3. Is responsible for the search module. Has 54 employees.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "react", "text": "Using version 15.3", "speaker": "user"}, {"entity": "nina_petrov", "text": "Is responsible for the search module", "speaker": "user"}, {"entity": "clearpath", "text": "Has 54 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:
[]

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

PREVIOUS ASSISTANT: null
USER: Te cuento del proyecto Nova. Es una marketplace que estamos armando en NovaTech. Stack: Django + Svelte, base de datos en MongoDB.

[Assistant] {"topic": {"action": "changed", "label": "Nova development"}, "entities": [{"id": "nova", "label": "Nova", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "nova", "target": "novatech", "relation": "part_of"}, {"source": "nova", "target": "svelte", "relation": "uses_technology"}, {"source": "nova", "target": "django", "relation": "uses_technology"}, {"source": "nova", "target": "mongodb", "relation": "uses_technology"}, {"source": "aisha_patel", "target": "novatech", "relation": "works_at"}, {"source": "aisha_patel", "target": "nova", "relation": "maintains"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "mexico_city", "label": "Mexico City", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "swimming", "label": "Swimming", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "running", "label": "Running", "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 Helix with Rails.

[Assistant] {"topic": {"action": "changed", "label": "Helix development"}, "entities": [{"id": "helix", "label": "Helix", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "helix", "target": "rails", "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": "london", "label": "London", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "swimming", "label": "Swimming", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "baking", "label": "Baking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: By the way, birthday is in march.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "raj_kapoor", "text": "Birthday is in March", "speaker": "user"}, {"entity": "raj_kapoor", "text": "Works as a doctor", "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: So we just kicked off Atlas at Acme Corp. It's a task manager. We're going with Flask on the backend, React on the frontend, and MySQL. Lisa Wong is handling the frontend.

[Assistant] {"topic": {"action": "changed", "label": "Atlas development"}, "entities": [{"id": "atlas", "label": "Atlas", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "atlas", "target": "acme_corp", "relation": "part_of"}, {"source": "atlas", "target": "react", "relation": "uses_technology"}, {"source": "atlas", "target": "flask", "relation": "uses_technology"}, {"source": "atlas", "target": "mysql", "relation": "uses_technology"}, {"source": "lisa_wong", "target": "acme_corp", "relation": "works_at"}, {"source": "lisa_wong", "target": "atlas", "relation": "maintains"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "meridian_software", "label": "Meridian Software", "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: unresolved — classify the topic yourself
CURRENT TOPIC: Some project

PREVIOUS ASSISTANT: null
USER: Nos vemos!

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


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

[User]
EXISTING NODES:
[{"id": "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": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "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 keyword match)
CURRENT TOPIC: Helix development

PREVIOUS ASSISTANT: Got it, tell me more about Helix.
USER: Omar Hassan arrancó esta semana como SRE. Y ahora usamos Python.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "python_lang", "label": "Python", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as SRE", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "helix", "target": "python_lang", "relation": "uses_technology"}, {"source": "omar_hassan", "target": "helix", "relation": "maintains"}, {"source": "omar_hassan", "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:
[{"id": "data_structures", "label": "Data Structures", "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": "eigenvalues", "label": "Eigenvalues", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "backpropagation", "label": "Backpropagation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "big_o_notation", "label": "Big O Notation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "pagerank", "label": "PageRank", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "quantum_physics", "label": "Quantum Physics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Data Structures course

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

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "pagerank", "text": "Key formula involves derivatives", "speaker": "user"}, {"entity": "prof_garcia", "text": "Has office hours on Thursdays", "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": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Actually, Sarah Johnson got promoted to full-stack developer.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "sarah_johnson", "text": "Now works as full-stack developer", "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_williams", "label": "Professor Williams", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gradient_descent", "label": "Gradient Descent", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "transformer_arch", "label": "Transformer Architecture", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "regularization", "label": "Regularization", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: We just started learning about Overfitting in class.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "overfitting", "label": "Overfitting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "overfitting", "target": "microeconomics", "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": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

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

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


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

[User]
EXISTING NODES:
[]

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

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

Architecture overview of Nova. Tech stack: Firebase, FastAPI, Redis, MongoDB. Key contributors: Lisa Wong, James O'Brien, Thomas Müller.

[Assistant] {"topic": {"action": "changed", "label": "Nova development"}, "entities": [{"id": "nova", "label": "Nova", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "nova_architecture_doc", "label": "Nova Architecture Document", "type": "document", "layer": "PERSONAL", "attributes": {"description": "Architecture overview of Nova. Tech stack: Firebase, FastAPI, Redis, MongoDB. Key contributors: Lisa"}, "facts": [], "existing_id": null}], "relations": [{"source": "nova", "target": "firebase_db", "relation": "uses_technology"}, {"source": "nova", "target": "fastapi", "relation": "uses_technology"}, {"source": "nova", "target": "redis", "relation": "uses_technology"}, {"source": "nova", "target": "mongodb", "relation": "uses_technology"}, {"source": "nova_architecture_doc", "target": "nova", "relation": "documented_in"}, {"source": "lisa_wong", "target": "nova", "relation": "maintains"}, {"source": "james_obrien", "target": "nova", "relation": "maintains"}, {"source": "thomas_muller", "target": "nova", "relation": "maintains"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "horizon", "label": "Horizon", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Harbor Systems"}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "spring_boot", "label": "Spring Boot", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "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": "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": {}}]

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

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

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "the_platform", "label": "The Platform", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "horizon"}], "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": "meridian_software", "label": "Meridian Software", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "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": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "orbit", "label": "Orbit", "type": "project", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: We had a interview session last week with Omar Hassan and Julia Santos and Priya Sharma. Interviewed candidates for CTO position.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "hiring_interview", "label": "Interview Session", "type": "event", "layer": "PERSONAL", "attributes": {"description": "Interviewed candidates for CTO position", "temporal_marker": "last week"}, "facts": [], "existing_id": null}], "relations": [{"source": "omar_hassan", "target": "hiring_interview", "relation": "participated_in"}, {"source": "julia_santos", "target": "hiring_interview", "relation": "participated_in"}, {"source": "priya_sharma", "target": "hiring_interview", "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": "flux", "label": "Flux", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at BlueShift Labs"}}, {"id": "blueshift_labs", "label": "BlueShift Labs", "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": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gcp", "label": "GCP", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "docker", "label": "Docker", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: We had a sprint review two days ago. Thomas Müller, Carlos Mendez were there. Reviewed Q2 deliverables.

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


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

[User]
EXISTING NODES:
[{"id": "pulse", "label": "Pulse", "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": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aws", "label": "AWS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "kubernetes", "label": "Kubernetes", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: The database is running slow on production.

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


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

[User]
EXISTING NODES:
[{"id": "mendoza", "label": "Mendoza", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "hiking", "label": "Hiking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

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

[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": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "prism", "label": "Prism", "type": "project", "layer": "PERSONAL", "attributes": {}}]

TOPIC HINT: subtopic of NovaTech team (medium confidence — verify)
CURRENT TOPIC: NovaTech team

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

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


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

[User]
EXISTING NODES:
[{"id": "pulse", "label": "Pulse", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Ironforge"}}, {"id": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: My friend Alice Chen asked me for help with something.

[Assistant] {"topic": {"action": "changed", "label": "Helping a friend"}, "entities": [{"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "snape", "label": "Severus Snape", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ron_weasley", "label": "Ron Weasley", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "dumbledore", "label": "Albus Dumbledore", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sirius_black", "label": "Sirius Black", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "draco_malfoy", "label": "Draco Malfoy", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hermione_granger", "label": "Hermione Granger", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "diagon_alley", "label": "Diagon Alley", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ministry_of_magic", "label": "Ministry of Magic", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: What else about Harry Potter?
USER: So in the book, is described as massive.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "diagon_alley", "text": "Is described as massive", "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 Turing Machine in Machine Learning":

This paper reviews Turing Machine and its relationship to Attention Mechanism. Authors: Professor Kumar. Key finding: reduced complexity.

[Assistant] {"topic": {"action": "changed", "label": "Machine Learning research"}, "entities": [{"id": "a_survey_of_turing_machine_in_machine_le", "label": "A Survey of Turing Machine in Machine Learning", "type": "document", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "turing_machine", "label": "Turing Machine", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "attention_mechanism", "label": "Attention Mechanism", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "prof_kumar", "label": "Professor Kumar", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "a_survey_of_turing_machine_in_machine_le", "target": "turing_machine", "relation": "documented_in"}, {"source": "a_survey_of_turing_machine_in_machine_le", "target": "attention_mechanism", "relation": "documented_in"}, {"source": "prof_kumar", "target": "a_survey_of_turing_machine_in_machine_le", "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": "berlin", "label": "Berlin", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "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": {}}, {"id": "painting", "label": "Painting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "swimming", "label": "Swimming", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

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

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


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

[User]
EXISTING NODES:
[{"id": "yoda", "label": "Yoda", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "obi_wan", "label": "Obi-Wan Kenobi", "type": "person", "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: Tell me more about Star Wars.
USER: New character: Luke Skywalker. They seem to have a history with Obi-Wan Kenobi.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "luke_skywalker", "label": "Luke Skywalker", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "luke_skywalker", "target": "obi_wan", "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": "calculus", "label": "Calculus", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_williams", "label": "Professor Williams", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "regularization", "label": "Regularization", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "transformer_arch", "label": "Transformer Architecture", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "big_o_notation", "label": "Big O Notation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "overfitting", "label": "Overfitting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "quantum_physics", "label": "Quantum Physics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "statistics", "label": "Statistics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

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

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "big_o_notation", "text": "Key formula involves derivatives", "speaker": "user"}, {"entity": "prof_williams", "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": "computer_networks", "label": "Computer Networks", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_kumar", "label": "Professor Kumar", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gradient_descent", "label": "Gradient Descent", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "regularization", "label": "Regularization", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "turing_machine", "label": "Turing Machine", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: We just started learning about Eigenvalues in class.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "eigenvalues", "label": "Eigenvalues", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "eigenvalues", "target": "computer_networks", "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": "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": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: What else about Catalyst?
USER: Let's dive into the CI/CD pipeline part of Catalyst.

[Assistant] {"topic": {"action": "subtopic", "label": "Catalyst CI/CD pipeline"}, "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": "microeconomics", "label": "Microeconomics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_nakamura", "label": "Professor Nakamura", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "big_o_notation", "label": "Big O Notation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "regularization", "label": "Regularization", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "pagerank", "label": "PageRank", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "quantum_physics", "label": "Quantum Physics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "statistics", "label": "Statistics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: About the course: has office hours on mondays.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "prof_nakamura", "text": "Has office hours on Mondays", "speaker": "user"}, {"entity": "microeconomics", "text": "Uses R 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": "summit", "label": "Summit", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Clearpath"}}, {"id": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

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

[Assistant] {"topic": {"action": "subtopic", "label": "Summit deployment"}, "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": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "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: NovaTech team

PREVIOUS ASSISTANT: null
USER: We're also starting Echo, a new initiative. Thomas Müller will lead it as QA engineer.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "echo_app", "label": "Echo", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as QA engineer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "echo_app", "target": "novatech", "relation": "part_of"}, {"source": "thomas_muller", "target": "echo_app", "relation": "maintains"}, {"source": "thomas_muller", "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": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Tell me more about Skyline Tech.
USER: Te cuento que revenue grew 25% last quarter.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "skyline_tech", "text": "Revenue grew 25% last quarter", "speaker": "user"}, {"entity": "clearpath", "text": "Been a client for 5 years", "speaker": "user"}]}


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

[User]
EXISTING NODES:
[{"id": "london", "label": "London", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "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": "guitar", "label": "Guitar", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

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

[Assistant] {"topic": {"action": "changed", "label": "Learning Nuxt"}, "entities": [{"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "helix", "label": "Helix", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Skyline Tech"}}, {"id": "skyline_tech", "label": "Skyline Tech", "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": "postgresql", "label": "PostgreSQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "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: unresolved — classify the topic yourself
CURRENT TOPIC: Helix development

PREVIOUS ASSISTANT: What else about Helix?
USER: Now about the payments specifically — how should we handle it?

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


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

[User]
EXISTING NODES:
[{"id": "data_structures", "label": "Data Structures", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_nakamura", "label": "Professor Nakamura", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "pagerank", "label": "PageRank", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "eigenvalues", "label": "Eigenvalues", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "backpropagation", "label": "Backpropagation", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "quantum_physics", "label": "Quantum Physics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

TOPIC HINT: unresolved — classify the topic yourself
CURRENT TOPIC: Data Structures course

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

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


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

[User]
EXISTING NODES:
[{"id": "mendoza", "label": "Mendoza", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "running", "label": "Running", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "knitting", "label": "Knitting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "painting", "label": "Painting", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hiking", "label": "Hiking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

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

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


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

[User]
EXISTING NODES:
[]

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

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

Architecture overview of Flux. Tech stack: MongoDB, Next.js, Flask. Key contributors: Bob Martinez, Lisa Wong, David Kim.

[Assistant] {"topic": {"action": "changed", "label": "Flux development"}, "entities": [{"id": "flux", "label": "Flux", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "flux_architecture_doc", "label": "Flux Architecture Document", "type": "document", "layer": "PERSONAL", "attributes": {"description": "Architecture overview of Flux. Tech stack: MongoDB, Next.js, Flask. Key contributors: Bob Martinez, "}, "facts": [], "existing_id": null}], "relations": [{"source": "flux", "target": "mongodb", "relation": "uses_technology"}, {"source": "flux", "target": "nextjs", "relation": "uses_technology"}, {"source": "flux", "target": "flask", "relation": "uses_technology"}, {"source": "flux_architecture_doc", "target": "flux", "relation": "documented_in"}, {"source": "bob_martinez", "target": "flux", "relation": "maintains"}, {"source": "lisa_wong", "target": "flux", "relation": "maintains"}, {"source": "david_kim", "target": "flux", "relation": "maintains"}], "facts": []}


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

[User]
EXISTING NODES:
[]

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

PREVIOUS ASSISTANT: null
USER: Hey, let me tell you about our project Summit. It's a CMS we're building at Pinnacle Digital. Stack is FastAPI + Nuxt, PostgreSQL for storage. Nina Petrov is the tech lead.

[Assistant] {"topic": {"action": "changed", "label": "Summit development"}, "entities": [{"id": "summit", "label": "Summit", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "postgresql", "label": "PostgreSQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "summit", "target": "pinnacle_digital", "relation": "part_of"}, {"source": "summit", "target": "nuxt", "relation": "uses_technology"}, {"source": "summit", "target": "fastapi", "relation": "uses_technology"}, {"source": "summit", "target": "postgresql", "relation": "uses_technology"}, {"source": "nina_petrov", "target": "pinnacle_digital", "relation": "works_at"}, {"source": "nina_petrov", "target": "summit", "relation": "maintains"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "barcelona", "label": "Barcelona", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "hiking", "label": "Hiking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: By the way, works as a engineer.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "marco_silva", "text": "Works as a engineer", "speaker": "user"}]}


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

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

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

PREVIOUS ASSISTANT: What would you like to know about Dune?
USER: Can we analyze Baron Harkonnen in more detail?

[Assistant] {"topic": {"action": "subtopic", "label": "Baron Harkonnen's character"}, "entities": [], "relations": [], "facts": []}