Triple
T9686547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uvinza District |
E234422
|
entity |
| Predicate | hasAdministrativeSeat |
P1474
|
FINISHED |
| Object | Uvinza |
E37146
|
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: Uvinza | Statement: [Uvinza District, hasAdministrativeSeat, Uvinza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uvinza Context triple: [Uvinza District, hasAdministrativeSeat, Uvinza]
-
A.
Uvinza
chosen
Uvinza is a town in western Tanzania known historically for its salt production and location along the Central Line railway in Kigoma Region.
-
B.
Nyamwezi
Nyamwezi is a Bantu language spoken primarily in northwestern Tanzania by the Nyamwezi people.
-
C.
Uvinza District
Uvinza District is an administrative district in western Tanzania known for its salt production and location along the Central Railway line.
-
D.
Mbalizi
Mbalizi is a town in southwestern Tanzania located within the Mbeya Region, known as a local commercial and transport hub for the surrounding rural areas.
-
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.
- 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_69ca84ca73208190957a900c8543bdcc |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9cd2dab481908e0d3fed28de9d40 |
completed | April 1, 2026, 10:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1910b7c148190b9061b1ce0520e8b |
completed | April 4, 2026, 10:30 p.m. |
Created at: March 30, 2026, 8:17 p.m.