Triple
T15970510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Chivero |
E387307
|
entity |
| Predicate | suppliesWaterTo |
P4102
|
FINISHED |
| Object | Harare |
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: Harare | Statement: [Lake Chivero, suppliesWaterTo, Harare]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harare Context triple: [Lake Chivero, suppliesWaterTo, Harare]
-
A.
Harare
chosen
Harare is the largest city and main economic, political, and cultural center of Zimbabwe.
-
B.
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.
-
C.
Masvingo
Masvingo is one of Zimbabwe’s oldest urban centers, located in the country’s southeastern region near the Great Zimbabwe ruins.
-
D.
Bulawayo
Bulawayo is Zimbabwe’s second-largest city and a major industrial, cultural, and transport hub in the southwestern part of the country.
-
E.
Marondera
Marondera is a town in eastern Zimbabwe known as an agricultural and educational center within the Mashonaland region.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157291214819088d65e984609e42c |
completed | April 16, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff794c8508190a444af7ce968c5da |
completed | May 10, 2026, 3:12 a.m. |
Created at: April 10, 2026, 4:54 a.m.