Triple
T6995958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zezuru |
E162214
|
entity |
| Predicate | spokenInCity |
P8343
|
FINISHED |
| Object | Chitungwiza |
E8616
|
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: Chitungwiza | Statement: [Zezuru, spokenInCity, Chitungwiza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chitungwiza Context triple: [Zezuru, spokenInCity, Chitungwiza]
-
A.
Masvingo
Masvingo is one of Zimbabwe’s oldest urban centers, located in the country’s southeastern region near the Great Zimbabwe ruins.
-
B.
Harare
chosen
Harare is the largest city and main economic, political, and cultural center of Zimbabwe.
-
C.
Nsanje
Nsanje is a town in southern Malawi near the border with Mozambique, known as a key transport and trading center in the Lower Shire Valley.
-
D.
Marondera
Marondera is a town in eastern Zimbabwe known as an agricultural and educational center within the Mashonaland region.
-
E.
Gisenyi
Gisenyi is a city in northwestern Rwanda on the shores of Lake Kivu, historically significant as one of the key sites affected during the 1994 Rwandan genocide.
- 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_69c68857ffc08190857dc62cd5253777 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dbec259c8190bb4cfbc1ff6fc786 |
completed | March 27, 2026, 7:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7883f0d988190b170901aea57d360 |
completed | March 28, 2026, 7:50 a.m. |
Created at: March 27, 2026, 2:32 p.m.