Triple
T21713862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kinyankole language |
E535971
|
entity |
| Predicate | primaryEthnicGroup |
P194
|
FINISHED |
| Object | Banyankole |
—
|
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: Banyankole | Statement: [Kinyankole language, primaryEthnicGroup, Banyankole]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Banyankole Context triple: [Kinyankole language, primaryEthnicGroup, Banyankole]
-
A.
Kasese
Kasese is a town in western Uganda that serves as a key gateway to Queen Elizabeth National Park and the Rwenzori Mountains.
-
B.
Bunyoro
Bunyoro is a traditional kingdom and historical region in western Uganda that was once a powerful pre-colonial African state.
-
C.
Rubanda District
Rubanda District is an administrative district in southwestern Uganda known for its hilly terrain and rural communities.
-
D.
Runyankole
chosen
Runyankole is a Bantu language spoken primarily by the Banyankole people in southwestern Uganda.
-
E.
Mbarara District
Mbarara District is an administrative district in southwestern Uganda known for its regional commercial center, agricultural activity, and role as a transport hub.
- 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_69e0c46c6dd88190a595375fa6ebd701 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efb5369be88190bafc10863d4d1bd7 |
completed | April 27, 2026, 7:12 p.m. |
Created at: April 16, 2026, 6:47 p.m.