Triple

T649070
Position Surface form Disambiguated ID Type / Status
Subject Davis E11304 entity
Predicate ticketingSystem P3383 FINISHED
Object CharlieCard E3327 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: CharlieCard | Statement: [Davis, ticketingSystem, CharlieCard]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: CharlieCard
Context triple: [Davis, ticketingSystem, CharlieCard]
  • A. CharlieCard chosen
    The CharlieCard is a reusable contactless smart card used to pay fares on Boston's MBTA public transit system.
  • B. 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.
  • 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. Calling Cards
    Calling Cards is a program section of the Telluride Film Festival that showcases emerging filmmakers’ early or breakthrough works.
  • E. Nol card
    The Nol card is a rechargeable smart card used for paying fares across Dubai’s public transportation network, including metro, buses, trams, and water buses.
  • 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_69a493266a2881909daf4c40f719dee8 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49f308f34819094ba28cfc786051e completed March 1, 2026, 8:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69a58039191481908b11bfadb36f0c13 completed March 2, 2026, 12:19 p.m.
Created at: March 1, 2026, 7:36 p.m.