Triple
T16680381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Egerton University |
E405322
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Nakuru County |
E387680
|
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: Nakuru County | Statement: [Egerton University, locatedIn, Nakuru County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nakuru County Context triple: [Egerton University, locatedIn, Nakuru County]
-
A.
Nakuru County
chosen
Nakuru County is a region in Kenya’s Rift Valley known for its lakes, wildlife, and agricultural activities.
-
B.
Nyandarua County
Nyandarua County is an administrative region in central Kenya known for its highland agriculture and proximity to the Aberdare Range.
-
C.
Kirinyaga County
Kirinyaga County is an administrative region in central Kenya known for its fertile agricultural land on the slopes of Mount Kenya and its production of tea, coffee, and horticultural crops.
-
D.
Kiambu County
Kiambu County is a largely peri-urban and agricultural county in central Kenya, bordering Nairobi and forming part of the greater Nairobi metropolitan area.
-
E.
Narok County
Narok County is a county in southwestern Kenya known for its vast savannah landscapes, rich Maasai culture, and world-famous wildlife tourism.
- 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_69d8838c28748190b3f5967c743940ab |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37d6f5cf481909e7628bbaa884e5a |
completed | April 18, 2026, 12:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0170db6c6881908f5670c8282f4097 |
completed | May 11, 2026, 6:02 a.m. |
Created at: April 10, 2026, 5:19 a.m.