Triple

T22253466
Position Surface form Disambiguated ID Type / Status
Subject Kimvita dialect E550037 entity
Predicate hasAlternativeName P39 FINISHED
Object Kiswahili cha Kimvita 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: Kiswahili cha Kimvita | Statement: [Kimvita dialect, hasAlternativeName, Kiswahili cha Kimvita]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kiswahili cha Kimvita
Context triple: [Kimvita dialect, hasAlternativeName, Kiswahili cha Kimvita]
  • A. Kiswah
    Kiswah is the ornate black cloth embroidered with Quranic verses that traditionally drapes and adorns the Kaaba in Mecca.
  • B. Mvomero
    Mvomero is a district and rural town in eastern Tanzania known for its agricultural activities within the Morogoro Region.
  • C. Chimwiini chosen
    Chimwiini is a Bantu language of the Sabaki subgroup spoken primarily along the southern Somali coast, closely related to Swahili.
  • D. Chitegu Chinte
    Chitegu Chinte is a notable film directed by acclaimed Indian filmmaker M.S. Sathyu.
  • E. Kijitonyama
    Kijitonyama is a residential and commercial neighborhood in Dar es Salaam, Tanzania, known as one of the urban wards within the Kinondoni District.
  • 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_69e11e42adb8819087714772ea606709 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f138c0c4f48190a75473a7835014f1 completed April 28, 2026, 10:46 p.m.
Created at: April 16, 2026, 8:39 p.m.