Triple

T7310780
Position Surface form Disambiguated ID Type / Status
Subject Gol Gol E168084 entity
Predicate federalDivision P1566 FINISHED
Object Farrer E119973 NE 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: Farrer | Statement: [Gol Gol, federalDivision, Farrer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Farrer
Context triple: [Gol Gol, federalDivision, Farrer]
  • A. Farrer chosen
    Farrer is an Australian federal electoral division in New South Wales, encompassing large rural and regional areas in the state’s southwest.
  • B. Farguson
    Farguson is an alternative spelling of the surname Ferguson, which is of Scottish origin.
  • C. Pagford
    Pagford is the fictional English village that serves as the primary setting of J.K. Rowling’s novel "The Casual Vacancy," characterized by its seemingly idyllic facade and underlying social tensions.
  • D. Fenner
    Fenner is an Australian federal electoral division in the Australian Capital Territory, represented in the House of Representatives.
  • E. Farris
    Farris is a surname most notably associated with Christine King Farris, an American educator, author, and the elder sister of Martin Luther King Jr.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69c6888d8e3c81909db79714903baf31 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6ebff866081909916796d1b72aee8 completed March 27, 2026, 8:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7e56b4178819087341903a168440b completed March 28, 2026, 2:27 p.m.
Created at: March 27, 2026, 3:02 p.m.