Triple

T11176613
Position Surface form Disambiguated ID Type / Status
Subject British Rail Class 332 E264428 entity
Predicate builder P3143 FINISHED
Object CAF E59649 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: CAF | Statement: [British Rail Class 332, builder, CAF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: CAF
Context triple: [British Rail Class 332, builder, CAF]
  • A. CAF
    CAF is the Confederation of African Football, the governing body for association football in Africa and one of FIFA’s six continental confederations.
  • B. CAF chosen
    CAF is a Spanish multinational company that designs and manufactures railway vehicles and related transport equipment used by metro systems worldwide.
  • C. CAF
    CAF is an abbreviation that can refer to various organizations or groups, most notably the Cactus Air Force, a World War II Allied air unit based on Guadalcanal.
  • D. CAF
    CAF is the commonly used abbreviation for the Chief of Air Force, the professional head of an air force service.
  • E. CAF
    CAF is a multilateral development bank that finances sustainable development and regional integration projects across Latin America and the Caribbean.
  • 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_69d6aa9dafac8190bd90d2c74f661aa7 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8987e1081909b28a0bdb866beae completed April 9, 2026, 5:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4838f19388190af6fde7d4275ce2a completed April 19, 2026, 7:26 a.m.
Created at: April 8, 2026, 9:29 p.m.