Triple
T10504792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mashonaland East Province |
E247757
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Marondera |
E260242
|
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: Marondera | Statement: [Mashonaland East Province, largestCity, Marondera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marondera Context triple: [Mashonaland East Province, largestCity, Marondera]
-
A.
Marondera
chosen
Marondera is a town in eastern Zimbabwe known as an agricultural and educational center within the Mashonaland region.
-
B.
Chinhoyi
Chinhoyi is a town in northern Zimbabwe known as an administrative center and for the nearby Chinhoyi Caves.
-
C.
Chegutu
Chegutu is a town in central northern Zimbabwe known for its agricultural activities and gold mining.
-
D.
Kapiri Mposhi
Kapiri Mposhi is a town in central Zambia that serves as a key rail and road junction linking the country to Tanzania and other regions.
-
E.
Manzini
Manzini is a major city in Eswatini that serves as an important commercial and transport hub of the country.
- 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_69d381c4aa948190942e1d803143fb0e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5099f4dec8190a9851739c8bc9a69 |
completed | April 7, 2026, 1:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90ddbbb3c8190873e8e3c27039b16 |
completed | April 10, 2026, 2:48 p.m. |
Created at: April 6, 2026, 12:26 p.m.