Triple
T2517200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bulawayo Railway Museum |
E55439
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Bulawayo, Zimbabwe |
E9766
|
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: Bulawayo, Zimbabwe | Statement: [Bulawayo Railway Museum, locatedIn, Bulawayo, Zimbabwe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bulawayo, Zimbabwe Context triple: [Bulawayo Railway Museum, locatedIn, Bulawayo, Zimbabwe]
-
A.
Bulawayo
chosen
Bulawayo is Zimbabwe’s second-largest city and a major industrial, cultural, and transport hub in the southwestern part of the country.
-
B.
Chivhu, Zimbabwe
Chivhu, Zimbabwe is a small town in central Zimbabwe known as an agricultural center and one of the country’s oldest European-settled communities.
-
C.
Marondera
Marondera is a town in eastern Zimbabwe known as an agricultural and educational center within the Mashonaland region.
-
D.
Harare
Harare is the largest city and main economic, political, and cultural center of Zimbabwe.
-
E.
Chinhoyi
Chinhoyi is a town in northern Zimbabwe known as an administrative center and for the nearby Chinhoyi Caves.
- 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_69ab49e4749c8190813311efd1630f1b |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd2111d28819099884a2bec5e0366 |
completed | March 7, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b34ba6d2cc819091d748b8af7ccbe6 |
completed | March 12, 2026, 11:26 p.m. |
Created at: March 6, 2026, 9:46 p.m.