Triple

T8640526
Position Surface form Disambiguated ID Type / Status
Subject Amazonian Kichwa E204633 entity
Predicate hasDialect P4251 FINISHED
Object Tena Kichwa E741173 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: Tena Kichwa | Statement: [Amazonian Kichwa, hasDialect, Tena Kichwa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tena Kichwa
Context triple: [Amazonian Kichwa, hasDialect, Tena Kichwa]
  • A. Tena Kichwa chosen
    Tena Kichwa is a variety of Amazonian Kichwa spoken around the town of Tena in Ecuador, closely associated with the Indigenous Kichwa communities of that region.
  • B. Gambiri Kati
    Gambiri Kati is an alternative name for the Tregami language, an Indo-Iranian language spoken in parts of eastern Afghanistan.
  • C. M’kira
    M’kira is a small town and commune located in Tizi Ouzou Province in northern Algeria, within the Kabylie region.
  • D. Matsigenka
    The Matsigenka are an Indigenous people of the Peruvian Amazon known for their forest-based subsistence lifestyle, distinct language, and rich shamanic and cosmological traditions.
  • E. Maanami Mamani
    Maanami Mamani is a writer best known for contributing to the work "Through the Wire."
  • 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_69ca834ca1c88190a11ffb0200342fac completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc47944d1c819081f448f14d04bf9d completed March 31, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69cebc340528819085c80b69d6d32a34 completed April 2, 2026, 6:57 p.m.
Created at: March 30, 2026, 6:28 p.m.