Triple

T6628898
Position Surface form Disambiguated ID Type / Status
Subject Union Station (Toronto) E149870 entity
Predicate servesLine P839 FINISHED
Object UP Express line E99224 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: UP Express line | Statement: [Union Station (Toronto), servesLine, UP Express line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: UP Express line
Context triple: [Union Station (Toronto), servesLine, UP Express line]
  • A. UP Express chosen
    UP Express is a dedicated airport rail link in Toronto that provides fast, frequent train service between Union Station downtown and Toronto Pearson International Airport.
  • B. UP Express rail link
    The UP Express rail link is a dedicated airport rail service in Toronto that connects Toronto Pearson International Airport with the city's downtown Union Station.
  • C. Airport Express Line
    The Airport Express Line is a high-speed Delhi Metro corridor that connects central Delhi with Indira Gandhi International Airport, providing fast transit for air travelers and commuters.
  • D. Universal Express
    Universal Express is a paid line-skipping system used at Universal theme parks to reduce wait times for popular attractions.
  • E. U Line
    U Line is a light metro line in the Seoul metropolitan area that serves the city of Uijeongbu with driverless trains on an elevated and mostly automated system.
  • 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_69c687ee50048190aa151765bef16193 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6afa4a08481908cb5a554f388f32a completed March 27, 2026, 4:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6e44f5f9c819088cfb4fd87887766 completed March 27, 2026, 8:10 p.m.
Created at: March 27, 2026, 1:59 p.m.