Triple

T9101008
Position Surface form Disambiguated ID Type / Status
Subject Mnyovniki E218151 entity
Predicate hasTicketingSystem P3383 FINISHED
Object Troika card accepted E249223 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: Troika card accepted | Statement: [Mnyovniki, hasTicketingSystem, Troika card accepted]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Troika card accepted
Context triple: [Mnyovniki, hasTicketingSystem, Troika card accepted]
  • A. 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.
  • B. Troika card chosen
    The Troika card is a reusable contactless smart card used for paying fares across Moscow’s public transportation system.
  • C. Metcard
    Metcard was Melbourne’s former magnetic stripe ticketing system used for public transport before the introduction of the Myki smartcard.
  • D. TAP card
    The TAP card is a reusable contactless smart card used to pay fares across public transit systems in the Los Angeles County region.
  • E. 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.
  • 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_69ca83d9844081908e561e367fda6d45 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc9711babc8190a336812dd08d9c73 completed April 1, 2026, 3:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0182d8ea08190b4337a77b47019a5 completed April 3, 2026, 7:42 p.m.
Created at: March 30, 2026, 7:15 p.m.