Triple
T7019351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jambojet |
E162778
|
entity |
| Predicate | cityServed |
P82
|
FINISHED |
| Object | Malindi |
E522404
|
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: Malindi | Statement: [Jambojet, cityServed, Malindi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malindi Context triple: [Jambojet, cityServed, 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.
Mombasa
Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
-
C.
Mambasa
Mambasa is a town and administrative center located in the forested Ituri region of northeastern Democratic Republic of the Congo.
-
D.
Umtentweni
Umtentweni is a coastal resort town on South Africa’s KwaZulu-Natal South Coast, known for its beaches, subtropical climate, and relaxed holiday atmosphere.
-
E.
Gombe
Gombe is a region in western Tanzania best known for its national park where pioneering primatologist Jane Goodall conducted her landmark chimpanzee research.
- 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_69c6885b26248190a857541e3d10e299 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e1e8e36c81908c95a8181781cda4 |
completed | March 27, 2026, 8 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7885104e881909be62c2eb12e0bcf |
completed | March 28, 2026, 7:50 a.m. |
Created at: March 27, 2026, 2:34 p.m.