Triple

T15476546
Position Surface form Disambiguated ID Type / Status
Subject Zhongshan Station E376793 entity
Predicate ticketingSystem P3383 FINISHED
Object EasyCard accepted E376162 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: EasyCard accepted | Statement: [Zhongshan Station, ticketingSystem, EasyCard accepted]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: EasyCard accepted
Context triple: [Zhongshan Station, ticketingSystem, EasyCard accepted]
  • A. EasyCard chosen
    EasyCard is a rechargeable contactless smart card widely used in Taipei and other parts of Taiwan for public transportation fares and small-value retail payments.
  • B. Presto card
    The Presto card is a reloadable smart card used for paying public transit fares across the Greater Toronto and Hamilton Area and other regions in Ontario, Canada.
  • C. Metcard
    Metcard was Melbourne’s former magnetic stripe ticketing system used for public transport before the introduction of the Myki smartcard.
  • D. Pronto card
    The Pronto card is a reloadable smart fare card used for paying public transit fares across the San Diego Metropolitan Transit System and related services.
  • E. EASY Card
    The EASY Card is a contactless smart card used as a stored-value payment method for public transit services in the Miami-Dade area.
  • 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_69d85cd21dcc81908646251b1c26ea00 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03f88a5dc8190a2d7830748e29180 completed April 16, 2026, 1:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff2d093ccc8190aefc355a837c83f4 completed May 9, 2026, 12:48 p.m.
Created at: April 10, 2026, 3:34 a.m.