Triple
T13712562
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Makoni District |
E328809
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Nyazura |
E331703
|
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: Nyazura | Statement: [Makoni District, hasSettlement, Nyazura]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nyazura Context triple: [Makoni District, hasSettlement, Nyazura]
-
A.
Nyazura
chosen
Nyazura is a small town in eastern Zimbabwe situated along the main road and railway linking Harare and Mutare.
-
B.
Mhangura
Mhangura is a small mining town in northern Zimbabwe known historically for its copper production.
-
C.
Maswa
Maswa is a town and administrative district in northern Tanzania, known for its agricultural activities within the Simiyu Region.
-
D.
Nyamira
Nyamira is a town in western Kenya that serves as an administrative and commercial center in the former Nyanza region.
-
E.
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.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd4395e8c0819098719c8cd344aa33 |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d54a68081908df25edf6d5df362 |
completed | May 3, 2026, 7:09 p.m. |
Created at: April 9, 2026, 9:54 p.m.