Triple

T14747446
Position Surface form Disambiguated ID Type / Status
Subject Hunter line E346508 entity
Predicate fareSystem P395 FINISHED
Object Opal card E98383 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: Opal card | Statement: [Hunter line, fareSystem, Opal card]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Opal card
Context triple: [Hunter line, fareSystem, Opal card]
  • A. Opal card chosen
    The Opal card is a reusable, contactless smartcard used to pay for public transport across much of New South Wales, Australia.
  • B. ORCA card
    The ORCA card is a reusable, contactless smart card used to pay fares across multiple public transit systems in the Puget Sound region of Washington State.
  • C. Myki
    Myki is Melbourne’s contactless smartcard public transport ticketing system used across trains, trams, and buses in Victoria, Australia.
  • D. Octopus card
    The Octopus card is a rechargeable contactless smart card widely used in Hong Kong for public transport fares and everyday electronic payments.
  • E. 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.
  • 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_69d822e6f1c88190bc494d491a907114 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec7d116e88190828b163b18d80f68 completed April 14, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69fee5dee8988190b80cb487c12bfc2d completed May 9, 2026, 7:44 a.m.
Created at: April 10, 2026, 1:30 a.m.