Triple
T16713436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenyan |
E406163
|
entity |
| Predicate | majorCity |
P316
|
FINISHED |
| Object | Kisumu |
E43852
|
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: Kisumu | Statement: [Kenyan, majorCity, Kisumu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kisumu Context triple: [Kenyan, majorCity, Kisumu]
-
A.
Kisumu
chosen
Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
-
B.
Kitale
Kitale is a town in western Kenya that serves as an important agricultural and commercial center near the slopes of Mount Elgon.
-
C.
Kabete
Kabete is a prominent town in Kenya’s Central Region, situated within Kiambu County and known for its agricultural activity and proximity to Nairobi.
-
D.
Isiolo
Isiolo is a town in central Kenya that serves as a key transport and commercial hub linking the country’s northern regions with the rest of the nation.
-
E.
Nakuru
Nakuru is a prominent Kenyan city in the Rift Valley region, known for its proximity to Lake Nakuru National Park and its role as an important agricultural and commercial center.
- 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_69d8838f242881908abd8bc138795886 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e38653cdd48190863e1cc989e21f39 |
completed | April 18, 2026, 1:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0091a95180819099abd50dd153e229 |
completed | May 10, 2026, 2:09 p.m. |
Created at: April 10, 2026, 5:20 a.m.