Triple
T21053969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kwale |
E518660
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object | Mombasa |
—
|
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: Mombasa | Statement: [Kwale, hasNearbyCity, Mombasa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mombasa Context triple: [Kwale, hasNearbyCity, Mombasa]
-
A.
Mombasa
chosen
Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
-
B.
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.
-
C.
Dar es Salaam
Dar es Salaam is a major coastal metropolis on the Indian Ocean and the principal economic and commercial hub of Tanzania.
-
D.
Port of Mombasa
The Port of Mombasa is Kenya’s largest and busiest seaport, serving as a key gateway for maritime trade in East and Central Africa.
-
E.
Juja
Juja is a rapidly growing urban town in Kenya known for its proximity to Nairobi and its major universities and industries.
- 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_69e0b5053ac48190921529544959e906 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fd7e087c81908712ddc63e8b1e6c |
completed | April 21, 2026, 4:30 a.m. |
Created at: April 16, 2026, 2:36 p.m.