Triple
T14309484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macheke |
E354785
|
entity |
| Predicate | roadConnectionTo |
P9041
|
FINISHED |
| Object | Mutare |
E10728
|
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: Mutare | Statement: [Macheke, roadConnectionTo, Mutare]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mutare Context triple: [Macheke, roadConnectionTo, Mutare]
-
A.
Mutare
chosen
Mutare is a major city in eastern Zimbabwe, serving as the capital of Manicaland Province and an important commercial and transport hub near the border with Mozambique.
-
B.
Masvingo
Masvingo is one of Zimbabwe’s oldest urban centers, located in the country’s southeastern region near the Great Zimbabwe ruins.
-
C.
Marondera
Marondera is a town in eastern Zimbabwe known as an agricultural and educational center within the Mashonaland region.
-
D.
Chitungwiza
Chitungwiza is a large high-density dormitory town in Zimbabwe situated just south of Harare, known for its rapid urban growth and vibrant informal economy.
-
E.
Cedza
Cedza is a Swazi prince and social entrepreneur known for his work in youth leadership and development initiatives.
- 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_69d8278ed42c8190b9f882dcce611347 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de85b26da48190a96e2f60ace51335 |
completed | April 14, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4684e2648190b46328252ac9d51b |
completed | May 8, 2026, 2:12 a.m. |
Created at: April 10, 2026, 1:12 a.m.