Triple

T20834624
Position Surface form Disambiguated ID Type / Status
Subject Kingaama E512926 entity
Predicate subclassOf P1244 FINISHED
Object Sabaki language NE NERFINISHED

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: Sabaki language | Statement: [Kingaama, subclassOf, Sabaki language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sabaki language
Context triple: [Kingaama, subclassOf, Sabaki language]
  • A. Sabaki languages chosen
    The Sabaki languages are a subgroup of Bantu languages spoken along the East African coast and nearby regions, including well-known varieties such as Swahili.
  • B. Marakwet language
    The Marakwet language is a Southern Nilotic language spoken by the Marakwet people of Kenya and is closely related to other Kalenjin languages such as Kipsigis.
  • C. Nyemba language
    The Nyemba language is a Bantu language spoken primarily by the Nyemba (Nyaneka-Nkhumbi) people of southwestern Angola.
  • D. Nyaturu language
    The Nyaturu language is a Bantu language spoken primarily by the Nyaturu people in central Tanzania.
  • E. Tembe language
    The Tembe language is an indigenous Tupi-Guarani language spoken by the Tembé people of northern Brazil.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4cf62a88190bbf92351e9e57259 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c32622c481908b8d2159bd5bb0ad completed April 21, 2026, 12:21 a.m.
Created at: April 16, 2026, 12:42 p.m.