Triple
T22808412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kadoma City Council |
E564601
|
entity |
| Predicate | jurisdiction |
P82
|
FINISHED |
| Object | City of Kadoma |
—
|
NE NERFINISHED |
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: City of Kadoma | Statement: [Kadoma City Council, jurisdiction, City of Kadoma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: City of Kadoma Context triple: [Kadoma City Council, jurisdiction, City of Kadoma]
-
A.
Kadoma
Kadoma is a city in Osaka Prefecture, Japan, known as a residential and commercial suburb within the Osaka metropolitan area.
-
B.
Kadoma
chosen
Kadoma is a city in central Zimbabwe known for its gold mining and agricultural activities.
-
C.
Mathare
Mathare is a densely populated informal settlement and neighborhood in Nairobi, Kenya, known for its extensive slums and socio-economic challenges.
-
D.
Kamukunji
Kamukunji is a densely populated, historically significant urban constituency and neighborhood in Nairobi known for its vibrant markets and political activism.
-
E.
Zaman Town
Zaman Town is a residential neighborhood located within the Korangi District of Karachi, Pakistan.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245823f4c8190ade442cdcc2c224a |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17d5e0b088190ad0b9cc0d5aa1d96 |
completed | April 29, 2026, 3:39 a.m. |
Created at: April 17, 2026, 3:32 p.m.