Triple

T10491529
Position Surface form Disambiguated ID Type / Status
Subject Arajuno Canton E247430 entity
Predicate hasIndigenousLanguage P4185 FINISHED
Object Kichwa E6374 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: Kichwa | Statement: [Arajuno Canton, hasIndigenousLanguage, Kichwa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kichwa
Context triple: [Arajuno Canton, hasIndigenousLanguage, Kichwa]
  • A. Kichwa chosen
    Kichwa is a Quechuan indigenous language variety widely spoken by Andean communities in Ecuador and neighboring regions.
  • B. Ikalanga
    Ikalanga is a Bantu language spoken primarily by the Kalanga people in Botswana and southwestern Zimbabwe.
  • C. Kwanyama
    Kwanyama is a major standardized dialect of the Ovambo language spoken primarily in northern Namibia and southern Angola.
  • D. Kwéyòl
    Kwéyòl is a French-based Creole language spoken primarily in the Lesser Antilles, notably in Saint Lucia and Dominica.
  • E. Kituba
    Kituba is a widely spoken Bantu-based creole language of Central Africa, serving as a major lingua franca in the Republic of the Congo and surrounding regions.
  • 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_69d381c309b88190af78aa681cf6a4c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5097e1c888190bc8e039f2e46181e completed April 7, 2026, 1:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69d933c5caa08190a5fba92ebf4b0ff9 completed April 10, 2026, 5:30 p.m.
Created at: April 6, 2026, 12:24 p.m.