Triple
T19799707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wankie Game Reserve |
E475638
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Hwange town |
—
|
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: Hwange town | Statement: [Wankie Game Reserve, near, Hwange town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hwange town Context triple: [Wankie Game Reserve, near, Hwange town]
-
A.
Hwange town
Hwange town is a settlement in western Zimbabwe best known as a gateway to the nearby Hwange National Park and its wildlife tourism.
-
B.
Tongayi
Tongayi is a Zimbabwean actor known for his roles in film and television, including appearances in international productions.
-
C.
Mazowe town
Mazowe town is a settlement in northern Zimbabwe known for its agricultural activities and proximity to the Mazowe River and surrounding farming estates.
-
D.
Chegutu
Chegutu is a town in central northern Zimbabwe known for its agricultural activities and gold mining.
-
E.
Hwange
chosen
Hwange is a town in western Zimbabwe best known for its large coal mining industry and its proximity to Hwange National Park, the country’s largest game reserve.
- 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e653cb865c81909696d2b37476f62f |
completed | April 20, 2026, 4:26 p.m. |
Created at: April 10, 2026, 1:49 p.m.