Triple
T15985470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nakuru County |
E387680
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Bomet County |
E865568
|
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: Bomet County | Statement: [Nakuru County, borders, Bomet County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bomet County Context triple: [Nakuru County, borders, Bomet County]
-
A.
Bomet County
chosen
Bomet County is an agricultural county in Kenya’s Rift Valley region, predominantly inhabited by the Kipsigis sub-group of the Kalenjin community.
-
B.
Busia County
Busia County is a county in western Kenya bordering Uganda, known for its diverse ethnic communities and its role as a key cross-border trade hub.
-
C.
Nyeri County
Nyeri County is a highland region in central Kenya known for its fertile agricultural land, scenic views of Mount Kenya, and as the birthplace of Nobel Peace Prize laureate Wangari Maathai.
-
D.
Kisii County
Kisii County is an administrative county in southwestern Kenya known for its fertile highlands, intensive agriculture, and vibrant Kisii (Abagusii) community.
-
E.
Makueni County
Makueni County is a semi-arid administrative region in southeastern Kenya known for its agriculture, water-scarcity challenges, and location along key transport and river basins.
- 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157589d78819091f7b9b1081dd6ad |
completed | April 16, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3cfc8d08190a02abc90c889c8e1 |
completed | May 9, 2026, 11:31 p.m. |
Created at: April 10, 2026, 4:54 a.m.