Triple

T30221158
Position Surface form Disambiguated ID Type / Status
Subject censor (Roman Republic) E768346 entity
Predicate requiredPreviousOffice P72167 FINISHED
Object consul 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: consul | Statement: [censor (Roman Republic), requiredPreviousOffice, consul]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: requiredPreviousOffice
Context triple: [censor (Roman Republic), requiredPreviousOffice, consul]
  • A. previousOffice chosen
    Indicates that one office or position was held immediately before another in a sequence of offices.
  • B. precededByOfficeHolder
    Indicates that one office holder directly held a position before another office holder in a sequence of occupants of the same office.
  • C. officePreviouslyHeldBy
    Indicates that a particular office or position was formerly occupied by a specified person or entity.
  • D. precededByOfficeHolderTermStart
    Indicates that the start of one office holder’s term occurs after and is directly preceded by the start of another office holder’s term.
  • E. precededByOfficeHolderTermEnd
    Indicates that one office holder’s term began immediately after the end of another office holder’s term for the same position.
  • 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_69f2247fd8b8819087fcf83cb7a05eb8 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69fe00dad1708190b6522476bebb43af completed May 8, 2026, 3:27 p.m.
PD Predicate disambiguation batch_69fdfc3717f48190bb50ac2919c8ef95 completed May 8, 2026, 3:07 p.m.
Created at: April 29, 2026, 7:35 p.m.