Triple

T1831628
Position Surface form Disambiguated ID Type / Status
Subject Administración Nacional de Educación Pública E40773 entity
Predicate abbreviation P43 FINISHED
Object ANEP E203887 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: ANEP | Statement: [Administración Nacional de Educación Pública, abbreviation, ANEP]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ANEP
Context triple: [Administración Nacional de Educación Pública, abbreviation, ANEP]
  • A. ANEP chosen
    ANEP is Uruguay’s National Administration of Public Education, the autonomous body responsible for overseeing and managing the country’s public education system.
  • B. ANE
    ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
  • C. ANPP
    ANPP is the acronym for Cuba’s unicameral national legislature, the National Assembly of People’s Power.
  • D. ANA
    ANA is the standard three-letter abbreviation used for the Anaheim Ducks, a professional ice hockey team in the National Hockey League.
  • E. ANA
    ANA is the commonly used abbreviation for the Afghan National Army, the former main land warfare branch of Afghanistan’s armed forces.
  • 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_69a8864644bc8190b2358ab897194ac1 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb022aef48190975b6d12fc6681ad completed March 7, 2026, 4:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69adc9b24f448190a3aa5a85a9106d71 completed March 8, 2026, 7:10 p.m.
Created at: March 4, 2026, 7:32 p.m.