Triple

T2907509
Position Surface form Disambiguated ID Type / Status
Subject Waterloo & City line E63600 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: [Waterloo & City line, ticketingSystem, Oyster]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oyster
Context triple: [Waterloo & City line, 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. 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.
  • D. Ika
    Ika is a Sanskrit-derived word meaning “one” or “unity,” used in the Indonesian national motto “Bhinneka Tunggal Ika” to express the idea of oneness amid diversity.
  • E. Pomfret
    Pomfret is a small, historic town in northeastern Connecticut known for its rural character, scenic landscapes, and prestigious boarding schools.
  • 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_69ab4c44ab448190b9411324e8a1fc1d completed March 6, 2026, 9:51 p.m.
NER Named-entity recognition batch_69abe0d0628c81909680af2f0db2ecae completed March 7, 2026, 8:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69b05612e79081908c962c2fe2e362d6 completed March 10, 2026, 5:34 p.m.
Created at: March 6, 2026, 10:11 p.m.