Triple
T7866966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nyamwezi people |
E182640
|
entity |
| Predicate | language |
P15
|
FINISHED |
| Object | Kinyamwezi |
E667767
|
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: Kinyamwezi | Statement: [Nyamwezi people, language, Kinyamwezi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kinyamwezi Context triple: [Nyamwezi people, language, Kinyamwezi]
-
A.
Kinyamwezi
chosen
Kinyamwezi is a Bantu language spoken primarily by the Nyamwezi people in central Tanzania.
-
B.
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.
-
C.
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.
-
D.
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.
-
E.
Kibondo
Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
- 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_69ca82894d9081908a832bfce71a4714 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb38464274819080f182b53783fa84 |
completed | March 31, 2026, 2:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbdf6fb33881908cf7bd68915aa6b4 |
completed | March 31, 2026, 2:51 p.m. |
Created at: March 30, 2026, 4:54 p.m.