Triple
T21199151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malindi Airport |
E522404
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Malindi |
—
|
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: Malindi | Statement: [Malindi Airport, serves, Malindi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malindi Context triple: [Malindi Airport, serves, Malindi]
-
A.
Malindi
chosen
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.
-
B.
Mbewuleni
Mbewuleni is a rural village in South Africa’s Eastern Cape province, best known as the birthplace of former South African president Thabo Mbeki.
-
C.
Mombasa
Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
-
D.
Maswa
Maswa is a town and administrative district in northern Tanzania, known for its agricultural activities within the Simiyu Region.
-
E.
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.
- 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.