Triple

T16020034
Position Surface form Disambiguated ID Type / Status
Subject Cracklin' Rosie E388574 entity
Predicate hasFictionalSubject P33843 FINISHED
Object a woman named Rosie representing wine 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: a woman named Rosie representing wine | Statement: [Cracklin' Rosie, hasFictionalSubject, a woman named Rosie representing wine]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasFictionalSubject
Context triple: [Cracklin' Rosie, hasFictionalSubject, a woman named Rosie representing wine]
  • A. hasFictionalType
    Indicates that an entity is associated with or classified under a particular type or category that is fictional rather than real.
  • B. hasFictionalContent
    Indicates that something contains or includes material that is imaginary, invented, or not intended to represent real events or facts.
  • C. hasFictionalScope
    Indicates that something pertains to, applies within, or is limited to a fictional or imagined context rather than real-world scope.
  • D. hasFictionalWork
    Indicates that one entity is the creator, owner, or source of a fictional work associated with another entity.
  • E. hasFictionalForm chosen
    Indicates that an entity has a counterpart or representation that exists within a fictional or imaginary context.
  • 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_69d86dabcb7c8190b6a39d6831d2fa1b completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1858a00888190b8505071575dc56f completed April 17, 2026, 12:57 a.m.
PD Predicate disambiguation batch_69e1826a4f7c8190aba6d4f1075141b0 completed April 17, 2026, 12:44 a.m.
Created at: April 10, 2026, 4:55 a.m.