[System] You are a knowledge extractor for a personal knowledge graph. Analyze the conversation 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": "luke_skywalker", "label": "Luke Skywalker", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "han_solo", "label": "Han Solo", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "coruscant", "label": "Coruscant", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "tatooine", "label": "Tatooine", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Changing topics — Microeconomics exam is coming up. Professor Smith's class.

[Assistant] {"topic": {"action": "changed", "label": "Microeconomics course"}, "entities": [{"id": "microeconomics", "label": "Microeconomics", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "prof_smith", "label": "Professor Smith", "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": "forge", "label": "Forge", "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": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gcp", "label": "GCP", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "kubernetes", "label": "Kubernetes", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Got it, tell me more about Forge.
USER: New update: we integrated Java into Forge. Also, Marco Silva is our new QA engineer.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "java_lang", "label": "Java", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as QA engineer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "forge", "target": "java_lang", "relation": "uses_technology"}, {"source": "marco_silva", "target": "forge", "relation": "maintains"}, {"source": "marco_silva", "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": "leto_atreides", "label": "Duke Leto Atreides", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "chani", "label": "Chani", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "jessica", "label": "Lady Jessica", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "stilgar", "label": "Stilgar", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "baron_harkonnen", "label": "Baron Harkonnen", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "caladan", "label": "Caladan", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "giedi_prime", "label": "Giedi Prime", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "arrakis", "label": "Arrakis", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

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

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "the_hero", "label": "The Hero", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": "baron_harkonnen"}], "relations": [], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "bariloche", "label": "Bariloche", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "baking", "label": "Baking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "running", "label": "Running", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

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

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


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

[User]
EXISTING NODES:
[{"id": "hagrid", "label": "Rubeus Hagrid", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sirius_black", "label": "Sirius Black", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mcgonagall", "label": "Minerva McGonagall", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ron_weasley", "label": "Ron Weasley", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ministry_of_magic", "label": "Ministry of Magic", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Who are Sirius Black's allies?

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


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

[User]
EXISTING NODES:
[{"id": "forge", "label": "Forge", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Vertex Solutions"}}, {"id": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gcp", "label": "GCP", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "jenkins", "label": "Jenkins", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Got it, tell me more about Forge.
USER: James O'Brien started this week as our new DevOps engineer. Oh, and we're now using Kotlin.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "kotlin_lang", "label": "Kotlin", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as DevOps engineer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "forge", "target": "kotlin_lang", "relation": "uses_technology"}, {"source": "james_obrien", "target": "forge", "relation": "maintains"}, {"source": "james_obrien", "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": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: We're also starting Compass, a new initiative. Raj Kapoor will lead it as product manager.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "compass", "label": "Compass", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as product manager", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "compass", "target": "acme_corp", "relation": "part_of"}, {"source": "raj_kapoor", "target": "compass", "relation": "maintains"}, {"source": "raj_kapoor", "target": "acme_corp", "relation": "works_at"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "tokyo", "label": "Tokyo", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "baking", "label": "Baking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "guitar", "label": "Guitar", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gardening", "label": "Gardening", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "chess", "label": "Chess", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: About the guitar thing — I've been making progress.

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


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

[User]
EXISTING NODES:
[{"id": "compass", "label": "Compass", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at NovaTech"}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "spring_boot", "label": "Spring Boot", "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": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "docker", "label": "Docker", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: What else about Compass?
USER: I want to focus on the CI/CD pipeline module. That's where we have issues.

[Assistant] {"topic": {"action": "subtopic", "label": "Compass 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": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "acme_corp", "label": "Acme Corp", "type": "organization", "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": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "compass", "label": "Compass", "type": "project", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: La empresa consiguió nueva financiación.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "la_empresa", "label": "La Empresa", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "blueshift_labs"}], "relations": [], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: New project: Helix. Priya Sharma (CTO) is heading it up.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "helix", "label": "Helix", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as CTO", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "helix", "target": "acme_corp", "relation": "part_of"}, {"source": "priya_sharma", "target": "helix", "relation": "maintains"}, {"source": "priya_sharma", "target": "acme_corp", "relation": "works_at"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "paris", "label": "Paris", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yoga", "label": "Yoga", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

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

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "paris", "text": "Relocated to Córdoba", "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": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "flux", "label": "Flux", "type": "project", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: We had a board meeting last week with Lisa Wong and Aisha Patel and Omar Hassan. Discussed Q3 results.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "board_meeting", "label": "Board Meeting", "type": "event", "layer": "PERSONAL", "attributes": {"description": "Discussed Q3 results", "temporal_marker": "last week"}, "facts": [], "existing_id": null}], "relations": [{"source": "lisa_wong", "target": "board_meeting", "relation": "participated_in"}, {"source": "aisha_patel", "target": "board_meeting", "relation": "participated_in"}, {"source": "omar_hassan", "target": "board_meeting", "relation": "participated_in"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "zenith", "label": "Zenith", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at BlueShift Labs"}}, {"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "supabase_db", "label": "Supabase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Hey, completely off topic — have you read Dune? I love Baron Harkonnen.

[Assistant] {"topic": {"action": "changed", "label": "Dune"}, "entities": [{"id": "baron_harkonnen", "label": "Baron Harkonnen", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "compass", "label": "Compass", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Ironforge"}}, {"id": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "angular", "label": "Angular", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "spring_boot", "label": "Spring Boot", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Got it, so Compass uses Spring Boot and Angular.
USER: Para que sepas, is responsible for the notifications module.

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


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

[User]
EXISTING NODES:
[]

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

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

[Assistant] {"topic": {"action": "changed", "label": "Harry Potter"}, "entities": [{"id": "sirius_black", "label": "Sirius Black", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "harry_potter", "label": "Harry Potter", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "hogwarts", "label": "Hogwarts", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "sirius_black", "target": "hogwarts", "relation": "located_in"}, {"source": "harry_potter", "target": "hogwarts", "relation": "located_in"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "baron_harkonnen", "label": "Baron Harkonnen", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "stilgar", "label": "Stilgar", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "chani", "label": "Chani", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "duncan_idaho", "label": "Duncan Idaho", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "caladan", "label": "Caladan", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Tell me more about Dune.
USER: There's also Lady Jessica who appears in the story. They interact with Baron Harkonnen a lot.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "jessica", "label": "Lady Jessica", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "jessica", "target": "baron_harkonnen", "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": "prism", "label": "Prism", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Clearpath"}}, {"id": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aws", "label": "AWS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "github_actions", "label": "GitHub Actions", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: I see, Prism is built with Next.js.
USER: Ah, me olvidé de decirte: has 328 tables.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "redis", "text": "Has 328 tables", "speaker": "user"}, {"entity": "prism", "text": "Has 1150 monthly active users", "speaker": "user"}, {"entity": "prism", "text": "Launched in June 2026", "speaker": "user"}]}


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

[User]
EXISTING NODES:
[{"id": "mexico_city", "label": "Mexico City", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "woodworking", "label": "Woodworking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "cooking", "label": "Cooking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: We had birthday party last month. Luca Rossi and Omar Hassan were there. Celebrated at a restaurant in Mexico City.

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


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

[User]
EXISTING NODES:
[{"id": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "flux", "label": "Flux", "type": "project", "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: Update: Revenue grew 7% last quarter.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "skyline_tech", "text": "Revenue grew 7% last quarter", "speaker": "user"}]}


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

[User]
EXISTING NODES:
[{"id": "mexico_city", "label": "Mexico City", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "photography", "label": "Photography", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "cycling", "label": "Cycling", "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: unresolved — classify the topic yourself
CURRENT TOPIC: Personal life

PREVIOUS ASSISTANT: null
USER: About the photography thing — I've been making progress.

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


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

[User]
EXISTING NODES:
[{"id": "barcelona", "label": "Barcelona", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gaming", "label": "Gaming", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "swimming", "label": "Swimming", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: My friend Sarah Johnson asked me for help with something.

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


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

[User]
EXISTING NODES:
[]

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

PREVIOUS ASSISTANT: Here's the chapter summary:
USER: Summary of Star Wars, Chapter 11:

Darth Vader travels to Endor where they encounter Leia Organa. A conflict arises when a betrayal is revealed. Darth Vader must decide whether to fight.

[Assistant] {"topic": {"action": "changed", "label": "Star Wars"}, "entities": [{"id": "darth_vader", "label": "Darth Vader", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "leia_organa", "label": "Leia Organa", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "han_solo", "label": "Han Solo", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "yoda", "label": "Yoda", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "endor", "label": "Endor", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "chapter_11_conflict", "label": "Chapter 11 Conflict", "type": "event", "layer": "UNIVERSAL", "attributes": {"description": "Conflict at Endor", "temporal_marker": "Chapter 11"}, "facts": [], "existing_id": null}], "relations": [{"source": "darth_vader", "target": "chapter_11_conflict", "relation": "participated_in"}, {"source": "leia_organa", "target": "chapter_11_conflict", "relation": "participated_in"}, {"source": "chapter_11_conflict", "target": "endor", "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": "london", "label": "London", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "reading", "label": "Reading", "type": "concept", "layer": "UNIVERSAL", "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: My uncle is Julia Santos, they live in London too.

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


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

[User]
EXISTING NODES:
[{"id": "strato", "label": "Strato", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Harbor Systems"}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Ah, me olvidé de decirte: has 421 tables.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "redis", "text": "Has 421 tables", "speaker": "user"}, {"entity": "strato", "text": "Has 20342 monthly active users", "speaker": "user"}, {"entity": "react", "text": "Using version 14.6", "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: I've processed the document. Here's what I found:
USER: Here's a summary of the document I just read:

Architecture overview of Beacon. Tech stack: Redis, MySQL, Firebase. Key contributors: Nina Petrov.

[Assistant] {"topic": {"action": "changed", "label": "Beacon development"}, "entities": [{"id": "beacon", "label": "Beacon", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "beacon_architecture_doc", "label": "Beacon Architecture Document", "type": "document", "layer": "PERSONAL", "attributes": {"description": "Architecture overview of Beacon. Tech stack: Redis, MySQL, Firebase. Key contributors: Nina Petrov."}, "facts": [], "existing_id": null}], "relations": [{"source": "beacon", "target": "redis", "relation": "uses_technology"}, {"source": "beacon", "target": "mysql", "relation": "uses_technology"}, {"source": "beacon", "target": "firebase_db", "relation": "uses_technology"}, {"source": "beacon_architecture_doc", "target": "beacon", "relation": "documented_in"}, {"source": "nina_petrov", "target": "beacon", "relation": "maintains"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "compass", "label": "Compass", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Clearpath"}}, {"id": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nextjs", "label": "Next.js", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Anything else about Compass?
USER: Off topic — have you been to Córdoba? I just got back.

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


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

[User]
EXISTING NODES:
[{"id": "skyline_tech", "label": "Skyline Tech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "beacon", "label": "Beacon", "type": "project", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: We had a interview session last week with James O'Brien and Priya Sharma. Interviewed candidates for SRE position.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "hiring_interview", "label": "Interview Session", "type": "event", "layer": "PERSONAL", "attributes": {"description": "Interviewed candidates for SRE position", "temporal_marker": "last week"}, "facts": [], "existing_id": null}], "relations": [{"source": "james_obrien", "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": "data_structures", "label": "Data Structures", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "prof_smith", "label": "Professor Smith", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "transformer_arch", "label": "Transformer Architecture", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "turing_machine", "label": "Turing Machine", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "eigenvalues", "label": "Eigenvalues", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: New topic in Data Structures: Gradient Descent.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "gradient_descent", "label": "Gradient Descent", "type": "concept", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "gradient_descent", "target": "data_structures", "relation": "part_of"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "horizon", "label": "Horizon", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Vertex Solutions"}}, {"id": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Hey, completely off topic — have you read Dune? I love Stilgar.

[Assistant] {"topic": {"action": "changed", "label": "Dune"}, "entities": [{"id": "stilgar", "label": "Stilgar", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "summit", "label": "Summit", "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": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Hey, completely off topic — have you read Lord of the Rings? I love Gollum.

[Assistant] {"topic": {"action": "changed", "label": "Lord of the Rings"}, "entities": [{"id": "gollum", "label": "Gollum", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "snape", "label": "Severus Snape", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mcgonagall", "label": "Minerva McGonagall", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ron_weasley", "label": "Ron Weasley", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "draco_malfoy", "label": "Draco Malfoy", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "harry_potter", "label": "Harry Potter", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sirius_black", "label": "Sirius Black", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hermione_granger", "label": "Hermione Granger", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "forbidden_forest", "label": "Forbidden Forest", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "privet_drive", "label": "4 Privet Drive", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "diagon_alley", "label": "Diagon Alley", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: What else about Harry Potter?
USER: In Harry Potter, is described as dangerous.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "forbidden_forest", "text": "Is described as dangerous", "speaker": "user"}]}


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

[User]
EXISTING NODES:
[{"id": "nexus", "label": "Nexus", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Pinnacle Digital"}}, {"id": "pinnacle_digital", "label": "Pinnacle Digital", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Got it, tell me more about Nexus.
USER: Sumamos Go al stack, y Julia Santos se unió como CTO.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "go_lang", "label": "Go", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as CTO", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "nexus", "target": "go_lang", "relation": "uses_technology"}, {"source": "julia_santos", "target": "nexus", "relation": "maintains"}, {"source": "julia_santos", "target": "pinnacle_digital", "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": "draco_malfoy", "label": "Draco Malfoy", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ron_weasley", "label": "Ron Weasley", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mcgonagall", "label": "Minerva McGonagall", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hogsmeade", "label": "Hogsmeade", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hogwarts", "label": "Hogwarts", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

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

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "draco_malfoy", "text": "Is described as wise", "speaker": "user"}, {"entity": "draco_malfoy", "text": "Has a special ability: the Force", "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": "orbit", "label": "Orbit", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Harbor Systems"}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "spring_boot", "label": "Spring Boot", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: How does the caching module work in Orbit?

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


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

[User]
EXISTING NODES:
[]

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

PREVIOUS ASSISTANT: null
USER: Hey, I'm part of the team at BlueShift Labs. We have a client called Ironforge. Key people: Omar Hassan is DevOps engineer, Raj Kapoor is backend developer.

[Assistant] {"topic": {"action": "changed", "label": "BlueShift Labs team"}, "entities": [{"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as DevOps engineer", "speaker": "user"}], "existing_id": null}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as backend developer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "blueshift_labs", "target": "ironforge", "relation": "serves"}, {"source": "omar_hassan", "target": "blueshift_labs", "relation": "works_at"}, {"source": "raj_kapoor", "target": "blueshift_labs", "relation": "works_at"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "summit", "label": "Summit", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Meridian Software"}}, {"id": "meridian_software", "label": "Meridian Software", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "flask", "label": "Flask", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yuki_tanaka", "label": "Yuki Tanaka", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "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": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "docker", "label": "Docker", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "bootstrap", "label": "Bootstrap", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "tailwind", "label": "Tailwind CSS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "terraform", "label": "Terraform", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "swift_lang", "label": "Swift", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

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

[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": "chani", "label": "Chani", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "leto_atreides", "label": "Duke Leto Atreides", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "paul_atreides", "label": "Paul Atreides", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "jessica", "label": "Lady Jessica", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "caladan", "label": "Caladan", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "arrakis", "label": "Arrakis", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Let's talk about Sandworm Ride — paul rides a sandworm for the first time. Key characters: Chani, Lady Jessica, Duke Leto Atreides.

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


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

[User]
EXISTING NODES:
[{"id": "helix", "label": "Helix", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Ironforge"}}, {"id": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "angular", "label": "Angular", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Quick update on Helix: Launched in June 2026. Has 17 tables. Currently in staging.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [], "relations": [], "facts": [{"entity": "helix", "text": "Launched in June 2026", "speaker": "user"}, {"entity": "mongodb", "text": "Has 17 tables", "speaker": "user"}, {"entity": "helix", "text": "Currently in staging", "speaker": "user"}]}


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

[User]
EXISTING NODES:
[{"id": "zenith", "label": "Zenith", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at NovaTech"}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "svelte", "label": "Svelte", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "rails", "label": "Rails", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: What else about Zenith?
USER: Ahora quiero hablar del payments específicamente.

[Assistant] {"topic": {"action": "subtopic", "label": "Zenith 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:
[]

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

PREVIOUS ASSISTANT: Here's the chapter summary:
USER: Summary of Dune, Chapter 6:

Duncan Idaho travels to Caladan where they encounter Chani. A conflict arises when a mysterious artifact is revealed. Duncan Idaho must decide whether to negotiate.

[Assistant] {"topic": {"action": "changed", "label": "Dune"}, "entities": [{"id": "duncan_idaho", "label": "Duncan Idaho", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "chani", "label": "Chani", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "leto_atreides", "label": "Duke Leto Atreides", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "caladan", "label": "Caladan", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "chapter_6_conflict", "label": "Chapter 6 Conflict", "type": "event", "layer": "UNIVERSAL", "attributes": {"description": "Conflict at Caladan", "temporal_marker": "Chapter 6"}, "facts": [], "existing_id": null}], "relations": [{"source": "duncan_idaho", "target": "chapter_6_conflict", "relation": "participated_in"}, {"source": "chani", "target": "chapter_6_conflict", "relation": "participated_in"}, {"source": "chapter_6_conflict", "target": "caladan", "relation": "located_in"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "bob_martinez", "label": "Bob Martinez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "atlas", "label": "Atlas", "type": "project", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Let's focus on the hiring plan now.

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


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

[User]
EXISTING NODES:
[{"id": "han_solo", "label": "Han Solo", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "obi_wan", "label": "Obi-Wan Kenobi", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "palpatine", "label": "Emperor Palpatine", "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: same (high confidence from keyword match)
CURRENT TOPIC: Star Wars

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

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "darth_vader", "label": "Darth Vader", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "darth_vader", "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": "apex", "label": "Apex", "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": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "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: unresolved — classify the topic yourself
CURRENT TOPIC: Apex development

PREVIOUS ASSISTANT: What else about Apex?
USER: Can we talk about the notifications for Apex?

[Assistant] {"topic": {"action": "subtopic", "label": "Apex 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": "cordoba_ar", "label": "Córdoba", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "cooking", "label": "Cooking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

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

[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": "legolas", "label": "Legolas", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sauron", "label": "Sauron", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "boromir", "label": "Boromir", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "the_shire", "label": "The Shire", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "isengard", "label": "Isengard", "type": "place", "layer": "UNIVERSAL", "attributes": {}}]

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

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

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


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

[User]
EXISTING NODES:
[{"id": "nova", "label": "Nova", "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": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "gcp", "label": "GCP", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "jenkins", "label": "Jenkins", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Got it, tell me more about Nova.
USER: Luca Rossi started this week as our new DevOps engineer. Oh, and we're now using Tailwind CSS.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "tailwind", "label": "Tailwind CSS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as DevOps engineer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "nova", "target": "tailwind", "relation": "uses_technology"}, {"source": "luca_rossi", "target": "nova", "relation": "maintains"}, {"source": "luca_rossi", "target": "blueshift_labs", "relation": "works_at"}], "facts": []}


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

[User]
EXISTING NODES:
[]

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

PREVIOUS ASSISTANT: null
USER: Te cuento del proyecto Nexus. Es una dashboard que estamos armando en Ironforge. Stack: FastAPI + Next.js, base de datos en PostgreSQL.

[Assistant] {"topic": {"action": "changed", "label": "Nexus development"}, "entities": [{"id": "nexus", "label": "Nexus", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "nextjs", "label": "Next.js", "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": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "nexus", "target": "ironforge", "relation": "part_of"}, {"source": "nexus", "target": "nextjs", "relation": "uses_technology"}, {"source": "nexus", "target": "fastapi", "relation": "uses_technology"}, {"source": "nexus", "target": "postgresql", "relation": "uses_technology"}, {"source": "maria_rodriguez", "target": "ironforge", "relation": "works_at"}, {"source": "maria_rodriguez", "target": "nexus", "relation": "maintains"}], "facts": []}


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

[User]
EXISTING NODES:
[]

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

PREVIOUS ASSISTANT: null
USER: Let's talk about Lord of the Rings. I'm really into Gimli and Boromir right now. The setting in Minas Tirith is amazing.

[Assistant] {"topic": {"action": "changed", "label": "Lord of the Rings"}, "entities": [{"id": "gimli", "label": "Gimli", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "boromir", "label": "Boromir", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "gandalf", "label": "Gandalf", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "minas_tirith", "label": "Minas Tirith", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "gimli", "target": "minas_tirith", "relation": "located_in"}, {"source": "boromir", "target": "minas_tirith", "relation": "located_in"}, {"source": "gandalf", "target": "minas_tirith", "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": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "blueshift_labs", "label": "BlueShift Labs", "type": "organization", "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": {}}]

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

PREVIOUS ASSISTANT: null
USER: Update: Priya Sharma moved to the tech lead position.

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


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

[User]
EXISTING NODES:
[{"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "julia_santos", "label": "Julia Santos", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "strato", "label": "Strato", "type": "project", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: La empresa consiguió nueva financiación.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "la_empresa", "label": "La Empresa", "type": "organization", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "harbor_systems"}], "relations": [], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "forge", "label": "Forge", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at NovaTech"}}, {"id": "novatech", "label": "NovaTech", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "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": "luca_rossi", "label": "Luca Rossi", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

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

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "la_base_de_datos", "label": "La Base De Datos", "type": "technology", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": "firebase_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": "vertex_solutions", "label": "Vertex Solutions", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "harbor_systems", "label": "Harbor Systems", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "lisa_wong", "label": "Lisa Wong", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "omar_hassan", "label": "Omar Hassan", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "horizon", "label": "Horizon", "type": "project", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: What's the budget for the Harbor Systems project?

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


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

[User]
EXISTING NODES:
[{"id": "apex", "label": "Apex", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Clearpath"}}, {"id": "clearpath", "label": "Clearpath", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "django", "label": "Django", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "firebase_db", "label": "Firebase", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "alice_chen", "label": "Alice Chen", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "jenkins", "label": "Jenkins", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

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

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


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

[User]
EXISTING NODES:
[{"id": "nova", "label": "Nova", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Meridian Software"}}, {"id": "meridian_software", "label": "Meridian Software", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "nuxt", "label": "Nuxt", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "maria_rodriguez", "label": "Maria Rodriguez", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

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

[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: MongoDB, MySQL, FastAPI. Key contributors: David Kim.

[Assistant] {"topic": {"action": "changed", "label": "Nova development"}, "entities": [{"id": "nova", "label": "Nova", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "mongodb", "label": "MongoDB", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "david_kim", "label": "David Kim", "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: MongoDB, MySQL, FastAPI. Key contributors: David Kim."}, "facts": [], "existing_id": null}], "relations": [{"source": "nova", "target": "mongodb", "relation": "uses_technology"}, {"source": "nova", "target": "mysql", "relation": "uses_technology"}, {"source": "nova", "target": "fastapi", "relation": "uses_technology"}, {"source": "nova_architecture_doc", "target": "nova", "relation": "documented_in"}, {"source": "david_kim", "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": "palpatine", "label": "Emperor Palpatine", "type": "person", "layer": "UNIVERSAL", "attributes": {}}, {"id": "yoda", "label": "Yoda", "type": "person", "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: null
USER: What's the significance of Death Star?

[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": "atlas", "label": "Atlas", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Ironforge"}}, {"id": "ironforge", "label": "Ironforge", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "angular", "label": "Angular", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "fastapi", "label": "FastAPI", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "postgresql", "label": "PostgreSQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "sarah_johnson", "label": "Sarah Johnson", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "aisha_patel", "label": "Aisha Patel", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "elena_volkov", "label": "Elena Volkov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "priya_sharma", "label": "Priya Sharma", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "azure", "label": "Azure", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "kubernetes", "label": "Kubernetes", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: What else about Atlas?
USER: The database schema is the next thing I need to figure out.

[Assistant] {"topic": {"action": "subtopic", "label": "Atlas database schema"}, "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": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "carlos_mendez", "label": "Carlos Mendez", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "thomas_muller", "label": "Thomas Müller", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "yoga", "label": "Yoga", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: We had dinner party two weeks ago. James O'Brien and Carlos Mendez were there. Hosted dinner with friends and family.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "dinner_party", "label": "Dinner Party", "type": "event", "layer": "PERSONAL", "attributes": {"description": "Hosted dinner with friends and family", "temporal_marker": "two weeks ago"}, "facts": [], "existing_id": null}], "relations": [{"source": "james_obrien", "target": "dinner_party", "relation": "participated_in"}, {"source": "carlos_mendez", "target": "dinner_party", "relation": "participated_in"}], "facts": []}


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

[User]
EXISTING NODES:
[{"id": "berlin", "label": "Berlin", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "james_obrien", "label": "James O'Brien", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "ahmed_farid", "label": "Ahmed Farid", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "hiking", "label": "Hiking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "gaming", "label": "Gaming", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: Different topic: Flux. We're building it with Express.

[Assistant] {"topic": {"action": "changed", "label": "Flux development"}, "entities": [{"id": "flux", "label": "Flux", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "flux", "target": "express", "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": "summit", "label": "Summit", "type": "project", "layer": "PERSONAL", "attributes": {"description": "Internal project at Acme Corp"}}, {"id": "acme_corp", "label": "Acme Corp", "type": "organization", "layer": "PERSONAL", "attributes": {}}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "express", "label": "Express", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "mysql", "label": "MySQL", "type": "technology", "layer": "UNIVERSAL", "attributes": {}}, {"id": "marco_silva", "label": "Marco Silva", "type": "person", "layer": "PERSONAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: Got it, tell me more about Summit.
USER: Sumamos AWS al stack, y Nina Petrov se unió como frontend developer.

[Assistant] {"topic": {"action": "same", "label": null}, "entities": [{"id": "aws", "label": "AWS", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "nina_petrov", "label": "Nina Petrov", "type": "person", "layer": "PERSONAL", "attributes": {}, "facts": [{"text": "Works as frontend developer", "speaker": "user"}], "existing_id": null}], "relations": [{"source": "summit", "target": "aws", "relation": "uses_technology"}, {"source": "nina_petrov", "target": "summit", "relation": "maintains"}, {"source": "nina_petrov", "target": "acme_corp", "relation": "works_at"}], "facts": []}


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

[User]
EXISTING NODES:
[]

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 Summit. Tech stack: Redis, React, Vue, MySQL. Key contributors: Bob Martinez.

[Assistant] {"topic": {"action": "changed", "label": "Summit development"}, "entities": [{"id": "summit", "label": "Summit", "type": "project", "layer": "PERSONAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "redis", "label": "Redis", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "react", "label": "React", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "vue", "label": "Vue", "type": "technology", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "mysql", "label": "MySQL", "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": "summit_architecture_doc", "label": "Summit Architecture Document", "type": "document", "layer": "PERSONAL", "attributes": {"description": "Architecture overview of Summit. Tech stack: Redis, React, Vue, MySQL. Key contributors: Bob Martine"}, "facts": [], "existing_id": null}], "relations": [{"source": "summit", "target": "redis", "relation": "uses_technology"}, {"source": "summit", "target": "react", "relation": "uses_technology"}, {"source": "summit", "target": "vue", "relation": "uses_technology"}, {"source": "summit", "target": "mysql", "relation": "uses_technology"}, {"source": "summit_architecture_doc", "target": "summit", "relation": "documented_in"}, {"source": "bob_martinez", "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": "london", "label": "London", "type": "place", "layer": "UNIVERSAL", "attributes": {}}, {"id": "david_kim", "label": "David Kim", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "raj_kapoor", "label": "Raj Kapoor", "type": "person", "layer": "PERSONAL", "attributes": {}}, {"id": "baking", "label": "Baking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}, {"id": "hiking", "label": "Hiking", "type": "concept", "layer": "UNIVERSAL", "attributes": {}}]

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

PREVIOUS ASSISTANT: null
USER: I should mention my uncle Aisha Patel.

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


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

[User]
EXISTING NODES:
[]

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

PREVIOUS ASSISTANT: null
USER: Let's talk about Star Wars. I'm really into Luke Skywalker and Yoda right now. The setting in Hoth is amazing.

[Assistant] {"topic": {"action": "changed", "label": "Star Wars"}, "entities": [{"id": "luke_skywalker", "label": "Luke Skywalker", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "yoda", "label": "Yoda", "type": "person", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}, {"id": "hoth", "label": "Hoth", "type": "place", "layer": "UNIVERSAL", "attributes": {}, "facts": [], "existing_id": null}], "relations": [{"source": "luke_skywalker", "target": "hoth", "relation": "located_in"}, {"source": "yoda", "target": "hoth", "relation": "located_in"}], "facts": []}