Triple

T14686350
Position Surface form Disambiguated ID Type / Status
Subject Green Square railway station E344916 entity
Predicate servesSuburb P82 FINISHED
Object Waterloo E873972 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: Waterloo | Statement: [Green Square railway station, servesSuburb, Waterloo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Waterloo
Context triple: [Green Square railway station, servesSuburb, Waterloo]
  • A. Waterloo
    Waterloo is a major district in central London known for its busy railway station, cultural venues like the Southbank Centre, and proximity to landmarks such as the London Eye and the River Thames.
  • B. Waterloo
    Waterloo was the original name of the settlement that later became the city of Austin, the capital of Texas.
  • C. Waterloo
    Waterloo is a village in North Lanarkshire, Scotland, forming part of the wider Wishaw area.
  • D. Waterloo
    Waterloo is a small village in eastern Nebraska, United States, located along the Elkhorn River just west of Omaha.
  • E. Waterloo chosen
    Waterloo is an inner-city suburb of Sydney, Australia, known for its mix of public housing, industrial heritage, and rapid urban redevelopment.
  • 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_69d822e34b348190ada4d1cdb6c7c226 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb58306548190b981956a83a84b95 completed April 14, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69fde1876fdc81908a4fe3deebb7ff83 completed May 8, 2026, 1:13 p.m.
Created at: April 10, 2026, 1:28 a.m.