Triple

T17922102
Position Surface form Disambiguated ID Type / Status
Subject ICE clearing network E448097 entity
Predicate component P35 FINISHED
Object ICE Clear Credit 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: ICE Clear Credit | Statement: [ICE clearing network, component, ICE Clear Credit]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ICE Clear Credit
Context triple: [ICE clearing network, component, ICE Clear Credit]
  • A. ICE Clear Credit chosen
    ICE Clear Credit is a central counterparty clearing house specializing in credit default swaps and other credit derivatives, providing risk management and clearing services to global financial markets.
  • B. Creditex
    Creditex is a financial services firm specializing in electronic trading and brokerage of credit derivatives and related fixed-income products.
  • C. Metcard
    Metcard was Melbourne’s former magnetic stripe ticketing system used for public transport before the introduction of the Myki smartcard.
  • D. 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.
  • E. JCB
    JCB is a major British multinational manufacturer of construction, agricultural, and industrial equipment, best known for its yellow diggers and backhoe loaders.
  • 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_69d8b9f6d394819082a6d69fd1e23d2f completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e4a30ad8748190b28d3e8b5afab2ef completed April 19, 2026, 9:40 a.m.
Created at: April 10, 2026, 10:20 a.m.