Triple
T6219520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | High Court of Zimbabwe |
E139074
|
entity |
| Predicate | hasSeatIn |
P3522
|
FINISHED |
| Object | Masvingo |
E11625
|
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: Masvingo | Statement: [High Court of Zimbabwe, hasSeatIn, Masvingo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Masvingo Context triple: [High Court of Zimbabwe, hasSeatIn, Masvingo]
-
A.
Masvingo
chosen
Masvingo is one of Zimbabwe’s oldest urban centers, located in the country’s southeastern region near the Great Zimbabwe ruins.
-
B.
Harare
Harare is the largest city and main economic, political, and cultural center of Zimbabwe.
-
C.
Mutare
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.
-
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_69c008aecb0c81909984b48f733ce8ae |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062bbb768819099402d367f124639 |
completed | March 22, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5190757648190a73575e680a35684 |
completed | March 26, 2026, 11:31 a.m. |
Created at: March 22, 2026, 4:21 p.m.