Triple
T19626463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wajir Air Base |
E471149
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Wajir, Kenya |
—
|
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: Wajir, Kenya | Statement: [Wajir Air Base, location, Wajir, Kenya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wajir, Kenya Context triple: [Wajir Air Base, location, Wajir, Kenya]
-
A.
Kamba (Kenya)
Kamba (Kenya) is a Bantu language spoken primarily by the Kamba people in eastern Kenya.
-
B.
Central Province, Kenya
Central Province, Kenya was a former administrative region in central Kenya known for its fertile highlands, tea and coffee production, and predominantly Kikuyu population.
-
C.
Kogelo, Kenya
Kogelo, Kenya is a rural village in Siaya County best known internationally as the ancestral home of former U.S. President Barack Obama’s family.
-
D.
Kajiado
Kajiado is a town in southern Kenya that serves as an administrative and commercial center for the surrounding Maasai-inhabited region.
-
E.
Wajir
chosen
Wajir is a major town in northeastern Kenya that serves as an important commercial and administrative center in a predominantly arid, pastoralist region.
- 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_69d8e511f28481909f4bc3ea9191e54a |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e640e9ff208190afb33c910ed2147b |
completed | April 20, 2026, 3:06 p.m. |
Created at: April 10, 2026, 1:44 p.m.