Triple

T6863938
Position Surface form Disambiguated ID Type / Status
Subject Myki E158348 entity
Predicate replacedSystem P3618 FINISHED
Object Metcard
Metcard was Melbourne’s former magnetic stripe ticketing system used for public transport before the introduction of the Myki smartcard.
E623257 NE FINISHED

How this triple was built (4 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: Metcard | Statement: [Myki, replacedSystem, Metcard]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Metcard
Context triple: [Myki, replacedSystem, Metcard]
  • 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. 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.
  • C. 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.
  • D. Breeze Card
    The Breeze Card is a reusable smart fare card used for paying transit fares across the Metropolitan Atlanta Rapid Transit Authority (MARTA) system in Atlanta, Georgia.
  • E. Creditex
    Creditex is a financial services firm specializing in electronic trading and brokerage of credit derivatives and related fixed-income products.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Metcard
Triple: [Myki, replacedSystem, Metcard]
Generated description
Metcard was Melbourne’s former magnetic stripe ticketing system used for public transport before the introduction of the Myki smartcard.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Metcard
Target entity description: Metcard was Melbourne’s former magnetic stripe ticketing system used for public transport before the introduction of the Myki smartcard.
  • 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. 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.
  • C. 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.
  • D. Breeze Card
    The Breeze Card is a reusable smart fare card used for paying transit fares across the Metropolitan Atlanta Rapid Transit Authority (MARTA) system in Atlanta, Georgia.
  • E. Creditex
    Creditex is a financial services firm specializing in electronic trading and brokerage of credit derivatives and related fixed-income products.
  • F. None of above. chosen

Provenance (5 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_69c68830cdbc8190a8301c7a9d9f651a completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d8897cf48190a2f688d21f1f6c1a completed March 27, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c72ff153d48190a4b0d4e403457fe8 completed March 28, 2026, 1:33 a.m.
NEDg Description generation batch_69c730e882308190a3fbc61245941338 completed March 28, 2026, 1:37 a.m.
NED2 Entity disambiguation (via description) batch_69c7316a9cc081908c088725cb7626e8 completed March 28, 2026, 1:39 a.m.
Created at: March 27, 2026, 2:21 p.m.