Triple

T11303727
Position Surface form Disambiguated ID Type / Status
Subject Gĩkũyũ E267660 entity
Predicate language P15 FINISHED
Object Gikuyu language E267659 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: Gikuyu language | Statement: [Gĩkũyũ, language, Gikuyu language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gikuyu language
Context triple: [Gĩkũyũ, language, Gikuyu language]
  • A. Gikuyu chosen
    Gikuyu is an alternative name for the Kikuyu, the largest ethnic group in Kenya known for their Bantu language and significant cultural and political influence in the country.
  • B. Swahili language
    Swahili is a major Bantu language widely spoken in East and Central Africa, serving as a regional lingua franca and an official language in several countries including Tanzania and Kenya.
  • C. Kinyankole language
    The Kinyankole language is a Bantu language spoken primarily by the Banyankole people in southwestern Uganda.
  • D. Ngindo language
    The Ngindo language is a Bantu language spoken by the Ngindo people of southeastern Tanzania.
  • E. Maasai language
    Maasai language is an Eastern Nilotic language spoken primarily by the Maasai people of Kenya and Tanzania, known for its rich oral tradition and distinctive phonology.
  • 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_69d6aac993a08190a6f36445ebaf9a43 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9a5c3788190ba54eda514b97903 completed April 9, 2026, 6:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69e525b4bdb88190b22d64eb65e97d9d completed April 19, 2026, 6:57 p.m.
Created at: April 8, 2026, 9:32 p.m.