Triple

T17534701
Position Surface form Disambiguated ID Type / Status
Subject Venlo railway station E427026 entity
Predicate ticketing P395 FINISHED
Object OV-chipkaart accepted NE NERFINISHED

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: OV-chipkaart accepted | Statement: [Venlo railway station, ticketing, OV-chipkaart accepted]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: OV-chipkaart accepted
Context triple: [Venlo railway station, ticketing, OV-chipkaart accepted]
  • A. OV-chipkaart chosen
    OV-chipkaart is the nationwide contactless smart card system used for paying public transport fares across the Netherlands.
  • B. 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.
  • C. 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.
  • D. OPUS card
    The OPUS card is a reusable, contactless smart card used for public transit fare payment across the greater Montreal area and other regions in Quebec.
  • E. Bilhete Único smart card
    The Bilhete Único smart card is São Paulo’s integrated public transport fare card, allowing seamless, discounted transfers across the city’s metro, bus, and train networks.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889de677081909b22d2657b1f0292 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e4536ac7f48190994f7b39a6a811d7 completed April 19, 2026, 4 a.m.
Created at: April 10, 2026, 5:49 a.m.