Triple

T1846703
Position Surface form Disambiguated ID Type / Status
Subject Kaspi Municipality E41298 entity
Predicate hasISO3166-1Alpha2Code P19525 FINISHED
Object GE E40849 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: GE | Statement: [Kaspi Municipality, hasISO3166-1Alpha2Code, GE]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: GE
Context triple: [Kaspi Municipality, hasISO3166-1Alpha2Code, GE]
  • A. GE chosen
    GE is the ISO 3166-1 alpha-2 country code for Georgia, a nation at the crossroads of Eastern Europe and Western Asia.
  • B. GE
    GE is the Swiss canton code for Geneva, a major city and canton in western Switzerland known for its international organizations and financial center.
  • C. General Electric
    General Electric is a major American multinational conglomerate historically known for its leadership in industrial manufacturing, aviation, power, and healthcare technologies.
  • D. General Motors
    General Motors is a major American multinational automotive manufacturer known for brands such as Chevrolet, GMC, Cadillac, and Buick.
  • E. Bosch
    Bosch is a multinational engineering and technology company best known for its automotive components, industrial products, and household appliances.
  • 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_69a88648cd44819093303206d96d76ad completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb052e0a8819091bbc0da0e0a20fb completed March 7, 2026, 4:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69adc9c2e0a081909f521e6f73956239 completed March 8, 2026, 7:10 p.m.
Created at: March 4, 2026, 7:33 p.m.