Triple

T494963
Position Surface form Disambiguated ID Type / Status
Subject Qantas E10272 entity
Predicate hub P423 FINISHED
Object Melbourne Airport E30111 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: Melbourne Airport | Statement: [Qantas, hub, Melbourne Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Melbourne Airport
Context triple: [Qantas, hub, Melbourne Airport]
  • A. Melbourne Airport (Tullamarine) chosen
    Melbourne Airport (Tullamarine) is the primary international and domestic airport serving the city of Melbourne, Australia.
  • B. Sydney Kingsford Smith Airport
    Sydney Kingsford Smith Airport is the primary international and domestic airport serving Sydney, Australia, and one of the busiest airports in the country.
  • C. Darwin International Airport
    Darwin International Airport is the main domestic and international airport serving the city of Darwin and the Northern Territory in northern Australia.
  • D. Newcastle Airport
    Newcastle Airport is a major regional airport in New South Wales, Australia, serving the city of Newcastle and the surrounding Hunter Region with domestic and limited international flights.
  • E. Hong Kong International Airport
    Hong Kong International Airport is a major global aviation hub and one of the world’s busiest passenger and cargo airports, serving Hong Kong with extensive international connections.
  • 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_69a2e847df8481909239ec08ccf1e376 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2f0fdd5608190815fa36485df8962 completed Feb. 28, 2026, 1:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69a481ee2e348190b26b02990b4fb866 completed March 1, 2026, 6:14 p.m.
Created at: Feb. 28, 2026, 1:12 p.m.