Triple

T12730964
Position Surface form Disambiguated ID Type / Status
Subject Battersea Power Station Underground station E304233 entity
Predicate ticketingSystem P3383 FINISHED
Object Oyster E293568 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: Oyster | Statement: [Battersea Power Station Underground station, ticketingSystem, Oyster]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oyster
Context triple: [Battersea Power Station Underground station, ticketingSystem, Oyster]
  • A. Oyster chosen
    Oyster is a contactless smartcard used for paying fares on public transport in London.
  • B. Mejillones
    Mejillones is a coastal Chilean port city on the Pacific Ocean, known for its fishing industry and role in regional maritime trade.
  • C. Shrimp
    Shrimp was the code name for the high-yield, solid-fueled thermonuclear device detonated in the United States' 1954 Castle Bravo nuclear test at Bikini Atoll.
  • D. Lamut
    Lamut is an indigenous Siberian people of northeastern Russia, more commonly known as the Even.
  • E. Dungeness crab
    The Dungeness crab is a large, commercially important crab species native to the Pacific coast of North America, prized for its sweet, tender meat.
  • 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_69d7bdf1426c8190a4402e1c4cdec33a completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96467a2248190aff1ebb5db84b3c6 completed April 10, 2026, 8:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69f684e7dec08190b522a8f3bfde6fe2 completed May 2, 2026, 11:12 p.m.
Created at: April 9, 2026, 5:25 p.m.