Triple

T16735977
Position Surface form Disambiguated ID Type / Status
Subject Kitanemuk E406717 entity
Predicate hasAlternateName P39 FINISHED
Object Kitanemuk language E278635 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: Kitanemuk language | Statement: [Kitanemuk, hasAlternateName, Kitanemuk language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kitanemuk language
Context triple: [Kitanemuk, hasAlternateName, Kitanemuk language]
  • A. Kitanemuk language chosen
    The Kitanemuk language is an extinct Uto-Aztecan language once spoken by the Kitanemuk people of Southern California.
  • B. Keiga language
    The Keiga language is a Kadu (Kadugli) language spoken by the Keiga people in the Nuba Mountains region of Sudan.
  • C. Mikasuki language
    The Mikasuki language is a Native American Muskogean language traditionally spoken by the Miccosukee and Seminole peoples of Florida.
  • D. Kawaiisu language
    Kawaiisu language is an endangered Uto-Aztecan language traditionally spoken by the Kawaiisu people of southern California.
  • E. Kiga language
    The Kiga language is a Bantu language spoken primarily by the Bakiga people of southwestern Uganda.
  • 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_69d8838ffb088190a0b11149929006bf completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e39c3a86848190a03f243dd1bdb899 completed April 18, 2026, 2:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a009d4ea8208190aed0a4014a10d120 completed May 10, 2026, 2:59 p.m.
Created at: April 10, 2026, 5:20 a.m.