Triple

T2610321
Position Surface form Disambiguated ID Type / Status
Subject Darling Harbour E58756 entity
Predicate locatedIn P40 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: [Darling Harbour, locatedIn, Sydney]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sydney
Context triple: [Darling Harbour, locatedIn, Sydney]
  • A. Sydney chosen
    Sydney is Australia's largest and most populous city, renowned for its iconic harbour, Opera House, and Harbour Bridge.
  • B. 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.
  • C. Melbourne
    Melbourne is a historic market town in Derbyshire, England, known for its Georgian architecture and the notable Melbourne Hall and gardens.
  • D. Brisbane
    Brisbane is the capital and most populous city of the Australian state of Queensland, known for its subtropical climate, riverfront setting, and role as a major economic and cultural hub.
  • E. Wollongong
    Wollongong is a coastal city in Australia known for its heavy industry, port facilities, and popular surf beaches along the Illawarra region.
  • 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_69ab4ac3523881909679750c9f8c2dec completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abd86a9c34819082623c4ee2c5069b completed March 7, 2026, 7:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69af9893d2048190ac3b36c32f14b63d completed March 10, 2026, 4:05 a.m.
Created at: March 6, 2026, 9:50 p.m.