Triple
T21199184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kilifi County |
E522405
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Kilifi |
—
|
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: Kilifi | Statement: [Kilifi County, hasSettlement, Kilifi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kilifi Context triple: [Kilifi County, hasSettlement, Kilifi]
-
A.
Kilifi
chosen
Kilifi is a coastal town in southeastern Kenya known for its beaches along the Indian Ocean and its role as an administrative and commercial center.
-
B.
Mombasa
Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
-
C.
Babati
Babati is a town in northern Tanzania that serves as an administrative and commercial hub near Lake Babati and the Tarangire National Park.
-
D.
Msambweni
Msambweni is a coastal town in southeastern Kenya known for its quiet beaches, fishing activities, and role as a local administrative and trading center.
-
E.
Kisumu
Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
- 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_69e0b51061388190aa03f19700d3ef04 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7342fe3a08190b7ed2cadf60091a8 |
completed | April 21, 2026, 8:24 a.m. |
Created at: April 16, 2026, 3:17 p.m.