Triple
T21588069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kisukuma language |
E532703
|
entity |
| Predicate | closelyRelatedTo |
P37
|
FINISHED |
| Object | Kinyamwezi |
—
|
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: Kinyamwezi | Statement: [Kisukuma language, closelyRelatedTo, Kinyamwezi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kinyamwezi Context triple: [Kisukuma language, closelyRelatedTo, 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.
Kinyara
Kinyara is a town in Uganda’s Masindi District, best known for its large sugar estate and associated agro-industrial activities.
-
D.
Mwiini
Mwiini is a Bantu language variety spoken along the southern Somali coast, closely related to Swahili and also known as Chimwiini.
-
E.
Manyoni
Manyoni is a town and district headquarters in central Tanzania known for its location along major road and rail routes in the Singida Region.
- 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_69e0c46251648190876f0427cf2d321b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eeeb621ab88190a33a943424ffb306 |
completed | April 27, 2026, 4:51 a.m. |
Created at: April 16, 2026, 6:31 p.m.