Triple
T13363107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naro Moru route |
E318868
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Nyeri County |
E590255
|
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: Nyeri County | Statement: [Naro Moru route, locatedIn, Nyeri County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nyeri County Context triple: [Naro Moru route, locatedIn, Nyeri County]
-
A.
Nyeri County
chosen
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.
-
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.
Bomet County
Bomet County is an agricultural county in Kenya’s Rift Valley region, predominantly inhabited by the Kipsigis sub-group of the Kalenjin community.
-
D.
Gunwi County
Gunwi County is a rural administrative region in South Korea known for its agricultural landscape and traditional Korean cultural heritage.
-
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_69d806b7bbac8190b85278c87fa7aff3 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69da628affd081909f1790d333f0eef4 |
completed | April 11, 2026, 3:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7267c99788190b158b1d9f57ceba2 |
completed | May 3, 2026, 10:42 a.m. |
Created at: April 9, 2026, 9:32 p.m.