Triple

T6190193
Position Surface form Disambiguated ID Type / Status
Subject Australian Classification Board E138166 entity
Predicate headquartersLocation P62 FINISHED
Object Sydney E8462 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: Sydney | Statement: [Australian Classification Board, headquartersLocation, Sydney]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sydney
Context triple: [Australian Classification Board, headquartersLocation, Sydney]
  • A. Sydney
    Sydney is a recurring character in Alison Bechdel’s long-running comic strip "Dykes to Watch Out For," known for her sharp intellect and complex personal relationships within its ensemble cast.
  • B. Sydney chosen
    Sydney is Australia's largest and most populous city, renowned for its iconic harbour, Opera House, and Harbour Bridge.
  • C. Sydney
    Sydney is the spirited, fashionable young woman who serves as the central heroine of Louisa May Alcott’s novel "An Old-Fashioned Girl."
  • D. Melbourne
    Melbourne is a major Australian city known for its vibrant arts scene, diverse culture, and status as a leading center for sports and education.
  • E. Melbourne
    Melbourne is a historic market town in Derbyshire, England, known for its Georgian architecture and the notable Melbourne Hall and gardens.
  • 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_69c008a8fd408190b7ec6e42934974a6 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0621abad48190acb9ec019c065ed4 completed March 22, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69c243bd199081909a96366cc39bf43e completed March 24, 2026, 7:56 a.m.
Created at: March 22, 2026, 4:19 p.m.