Triple

T626983
Position Surface form Disambiguated ID Type / Status
Subject F train E15839 entity
Predicate fareMediumAccepted P9955 FINISHED
Object MetroCard E3073 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: MetroCard | Statement: [F train, fareMediumAccepted, MetroCard]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MetroCard
Context triple: [F train, fareMediumAccepted, MetroCard]
  • A. MetroCard chosen
    MetroCard is a magnetic stripe payment card formerly used as the primary method for paying fares on New York City’s public transit system, including subways and buses.
  • B. SmarTrip
    SmarTrip is a rechargeable contactless smart card used to pay fares on the Washington, D.C. region’s public transit systems.
  • C. Oyster card
    The Oyster card is a rechargeable smartcard used for convenient, cashless payment on public transport services across London.
  • D. Clipper card
    The Clipper card is a reloadable contactless smart card used to pay fares across multiple public transit systems in the San Francisco Bay Area.
  • E. OMNY
    OMNY is the contactless fare payment system used by New York City's public transit network, replacing the MetroCard with tap-and-go payments via cards, phones, and wearables.
  • 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_69a4935c131c8190a5378c6bf101e8cc completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49e587c448190987943a6aad209d1 completed March 1, 2026, 8:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69a56935ec748190984b9cb6add6b700 completed March 2, 2026, 10:40 a.m.
Created at: March 1, 2026, 7:35 p.m.