Triple

T28450329
Position Surface form Disambiguated ID Type / Status
Subject Constable Kevin Goody E716558 entity
Predicate fictionalProfessionRank P116932 FINISHED
Object constable 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: constable | Statement: [Constable Kevin Goody, fictionalProfessionRank, constable]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: fictionalProfessionRank
Context triple: [Constable Kevin Goody, fictionalProfessionRank, constable]
  • 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. fictionalProfessionSpecialty
    Indicates that a fictional character’s professional role is specialized in a particular subfield, focus area, or niche within that profession.
  • C. hasFictionalProfessionLevel chosen
    Indicates that an entity holds a fictional or imagined profession at a specified level, rank, or degree of expertise.
  • D. fictionalCareerStatus
    Indicates the relationship between an entity and a career or professional role that exists only in a fictional or imagined context, rather than in real life.
  • E. laterOccupationInFiction
    Indicates that a fictional character holds a particular occupation at a later point in the narrative or timeline, distinct from their earlier roles.
  • 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_69efd6b76f8c8190a7ba908aca280942 completed April 27, 2026, 9:35 p.m.
NER Named-entity recognition batch_69f7979a073881909a4fde2558e6b6f3 completed May 3, 2026, 6:44 p.m.
PD Predicate disambiguation batch_69f7961550f88190b7bb8a9155458b54 completed May 3, 2026, 6:38 p.m.
Created at: April 28, 2026, 1:51 a.m.