Triple

T7492860
Position Surface form Disambiguated ID Type / Status
Subject Nyamwezi language E177048 entity
Predicate hasAlternativeName P39 FINISHED
Object Kinyamwezi
Kinyamwezi is a Bantu language spoken primarily by the Nyamwezi people in central Tanzania.
 E667767 NE FINISHED

How this triple was built (4 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: Kinyamwezi | Statement: [Nyamwezi language, hasAlternativeName, Kinyamwezi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kinyamwezi
Context triple: [Nyamwezi language, hasAlternativeName, Kinyamwezi]
  • A. Ntumu
    Ntumu is a dialect of the Fang language spoken by Fang communities in parts of Central Africa, particularly in Equatorial Guinea, Gabon, and Cameroon.
  • B. Mwenezi
    Mwenezi is a rural district and communal area in southern Zimbabwe known for cattle ranching, sugar estates, and its location along the Mwenezi River in Masvingo Province.
  • C. Cinyanja
    Cinyanja is a Bantu language spoken primarily in Malawi, Zambia, Mozambique, and Zimbabwe, where it serves as an important lingua franca in parts of southern Africa.
  • D. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • E. Nyazura
    Nyazura is a small town in eastern Zimbabwe situated along the main road and railway linking Harare and Mutare.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kinyamwezi
Triple: [Nyamwezi language, hasAlternativeName, Kinyamwezi]
Generated description
Kinyamwezi is a Bantu language spoken primarily by the Nyamwezi people in central Tanzania.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kinyamwezi
Target entity description: Kinyamwezi is a Bantu language spoken primarily by the Nyamwezi people in central Tanzania.
  • A. Ntumu
    Ntumu is a dialect of the Fang language spoken by Fang communities in parts of Central Africa, particularly in Equatorial Guinea, Gabon, and Cameroon.
  • B. Mwenezi
    Mwenezi is a rural district and communal area in southern Zimbabwe known for cattle ranching, sugar estates, and its location along the Mwenezi River in Masvingo Province.
  • C. Cinyanja
    Cinyanja is a Bantu language spoken primarily in Malawi, Zambia, Mozambique, and Zimbabwe, where it serves as an important lingua franca in parts of southern Africa.
  • D. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • E. Nyazura
    Nyazura is a small town in eastern Zimbabwe situated along the main road and railway linking Harare and Mutare.
  • F. None of above. chosen

Provenance (5 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_69c69f2583808190bd1a4936c42a5815 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f5784c908190b701959daf082625 completed March 27, 2026, 9:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83c7c60b8819090f2c4b16332c557 completed March 28, 2026, 8:39 p.m.
NEDg Description generation batch_69c83e50fac08190bdcceef0c5f55244 completed March 28, 2026, 8:47 p.m.
NED2 Entity disambiguation (via description) batch_69c83ebff75c81908313e6df6f76d3f0 completed March 28, 2026, 8:49 p.m.
Created at: March 27, 2026, 3:43 p.m.