Triple
T5429228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coast Province, Kenya |
E121441
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Kilifi |
E522405
|
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: Kilifi | Statement: [Coast Province, Kenya, containsCity, Kilifi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kilifi Context triple: [Coast Province, Kenya, containsCity, Kilifi]
-
A.
Mombasa
Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
-
B.
Kisumu
Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
-
C.
Malindi
Malindi is a historic coastal town in southeastern Kenya known for its beaches, Swahili culture, and role as a former trading port on the Indian Ocean.
-
D.
Kilifi County
chosen
Kilifi County is an administrative region along Kenya’s Indian Ocean coast, known for its beaches, Swahili culture, and tourism-centered economy.
-
E.
Omuta
Omuta is an industrial city in southern Fukuoka Prefecture, Japan, historically known for its coal mining and chemical industries.
- 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_69bd463c65f0819082ee6483ab4b466a |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd883d1bfc8190859bb05cfab065c8 |
completed | March 20, 2026, 5:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf6c548a40819086910fbc39b21e90 |
completed | March 22, 2026, 4:13 a.m. |
Created at: March 20, 2026, 2:06 p.m.