Triple

T38242747
Position Surface form Disambiguated ID Type / Status
Subject Guy Patterson E1013810 entity
Predicate fictionalFutureCareer P104743 FINISHED
Object jazz drummer LITERAL FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: jazz drummer | Statement: [Guy Patterson, fictionalFutureCareer, jazz drummer]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: fictionalFutureCareer
Context triple: [Guy Patterson, fictionalFutureCareer, jazz drummer]
  • A. fictionalOccupation
    Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
  • B. fictionalProfessionContext
    Indicates that an entity’s profession is defined or understood within a fictional, narrative, or imaginative context rather than as a real-world occupation.
  • C. visionaryOccupation
    Indicates that an entity holds an occupation or role characterized by forward-thinking, innovative, or visionary activities or responsibilities.
  • D. laterOccupationInFiction chosen
    Indicates that a fictional character holds a particular occupation at a later point in the narrative or timeline, distinct from their earlier roles.
  • E. fictionalProfessionSpecialty
    Indicates that a fictional character’s professional role is specialized in a particular subfield, focus area, or niche within that profession.
  • F. None of above.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69f76dd7e89c8190b7866bc85aea521b completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69ff795d25d08190b7584c72be39d309 completed May 9, 2026, 6:13 p.m.
PD Predicate disambiguation batch_69ff78a90fbc8190a62c57456dc1d4ad completed May 9, 2026, 6:10 p.m.
Created at: May 3, 2026, 4:30 p.m.