Triple

T12097539
Position Surface form Disambiguated ID Type / Status
Subject Ate District E288108 entity
Predicate alternativeName P39 FINISHED
Object Ate-Vitarte E427051 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: Ate-Vitarte | Statement: [Ate District, alternativeName, Ate-Vitarte]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ate-Vitarte
Context triple: [Ate District, alternativeName, Ate-Vitarte]
  • A. Ate-Vitarte chosen
    Ate-Vitarte is a district in the eastern part of Lima, Peru, known for its rapid urban growth and mixed residential and industrial areas.
  • B. Laguindingan
    Laguindingan is a coastal municipality in Misamis Oriental, Philippines, best known for hosting the Laguindingan Airport that serves the Cagayan de Oro–Iligan corridor.
  • C. Don Juan Panganiban
    Don Juan Panganiban is a character in the Ilocano epic "Biag ni Lam-ang," known as one of the notable figures surrounding the hero Lam-ang’s adventures.
  • D. Diosdado
    Diosdado is a Filipino given name most prominently associated with Diosdado Macapagal, the ninth President of the Philippines.
  • E. Kalepa Baybayan
    Kalepa Baybayan was a renowned Native Hawaiian master navigator and cultural educator known for reviving and teaching traditional Polynesian wayfinding techniques on voyaging canoes such as Hōkūleʻa.
  • 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_69d6ab4964708190850585628b287b0c completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d91552645c81909aff601ab3d3c0e6 completed April 10, 2026, 3:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f60a6f22cc8190ba12c910c5ef5868 completed May 2, 2026, 2:30 p.m.
Created at: April 8, 2026, 9:48 p.m.