Triple
T7417206
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neyagawa |
E171159
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Kadoma |
E134780
|
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: Kadoma | Statement: [Neyagawa, borderedBy, Kadoma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kadoma Context triple: [Neyagawa, borderedBy, Kadoma]
-
A.
Kadoma
chosen
Kadoma is a city in Osaka Prefecture, Japan, known as a residential and commercial suburb within the Osaka metropolitan area.
-
B.
Omuta
Omuta is an industrial city in southern Fukuoka Prefecture, Japan, historically known for its coal mining and chemical industries.
-
C.
Mathare
Mathare is a densely populated informal settlement and neighborhood in Nairobi, Kenya, known for its extensive slums and socio-economic challenges.
-
D.
Oshakati
Oshakati is a major northern Namibian town that serves as an important commercial and administrative hub.
-
E.
Kalangoya
Kalangoya is an alternative name for the Kalanguya language, an Austronesian language spoken by indigenous communities in the northern Philippines.
- 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_69c68a618bdc81908d8018edadecd1a4 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f2c7ae0c8190a8348d6223aeeecc |
completed | March 27, 2026, 9:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c81eed92888190bf9d11ab91378fa9 |
completed | March 28, 2026, 6:33 p.m. |
Created at: March 27, 2026, 3:11 p.m.