Triple

T1895690
Position Surface form Disambiguated ID Type / Status
Subject Dobutsuen-mae Station E41975 entity
Predicate ticketingSystem P3383 FINISHED
Object ICOCA E71900 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: ICOCA | Statement: [Dobutsuen-mae Station, ticketingSystem, ICOCA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ICOCA
Context triple: [Dobutsuen-mae Station, ticketingSystem, ICOCA]
  • A. ICOCA chosen
    ICOCA is a rechargeable contactless smart card used for fare payment on public transportation systems in the Kansai region of Japan.
  • B. Suica
    Suica is a rechargeable contactless smart card issued by JR East that is widely used for train fares and electronic payments across Japan.
  • C. PASMO
    PASMO is a rechargeable contactless smart card widely used for public transportation and electronic payments across the Tokyo metropolitan area.
  • D. ORCA card
    The ORCA card is a reusable, contactless smart card used to pay fares across multiple public transit systems in the Puget Sound region of Washington State.
  • E. Myki
    Myki is Melbourne’s contactless smartcard public transport ticketing system used across trains, trams, and buses in Victoria, Australia.
  • 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_69a8864b6de0819098d089f6a1b910a7 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb16d6674819084a891e3bf23bd83 completed March 7, 2026, 5:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69adeaebd5ec8190897619628f7b821d completed March 8, 2026, 9:32 p.m.
Created at: March 4, 2026, 7:35 p.m.