Triple

T4362740
Position Surface form Disambiguated ID Type / Status
Subject Girobank E98697 entity
Predicate hasAbbreviation P43 FINISHED
Object Girobank E98697 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: Girobank | Statement: [Girobank, hasAbbreviation, Girobank]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Girobank
Context triple: [Girobank, hasAbbreviation, Girobank]
  • A. Girobank chosen
    Girobank was a British state-owned financial institution that provided banking and giro transfer services, originally established and run through the national postal system.
  • B. Gosbank
    Gosbank was the central bank of the Soviet Union, responsible for issuing currency and overseeing the state-controlled financial system.
  • C. Worldline
    Worldline is a French multinational company specializing in payment and transactional services, recognized as a major European player in digital payments.
  • D. Equator Bank
    Equator Bank was a financial institution where future Liberian president and economist Ellen Johnson Sirleaf held a professional position during her banking career.
  • E. Aurora Bank
    Aurora Bank is a notable geological feature within the Scotia Arc, forming part of the submarine topography in the South Atlantic 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_69b3454c772081908e20173e379e8ebe completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b351e5ee308190a9271e73689b4a2b completed March 12, 2026, 11:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5dbc68534819095ce62645ca79eff completed March 14, 2026, 10:05 p.m.
Created at: March 12, 2026, 11:16 p.m.