Triple
T7019416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Precision Air |
E162779
|
entity |
| Predicate | cityServed |
P82
|
FINISHED |
| Object | Kigoma |
E34026
|
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: Kigoma | Statement: [Precision Air, cityServed, Kigoma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kigoma Context triple: [Precision Air, cityServed, Kigoma]
-
A.
Kigoma
chosen
Kigoma is a port city in western Tanzania located on the eastern shore of Lake Tanganyika and serving as a key regional transport and trade hub.
-
B.
Bukoba
Bukoba is a town on the western shore of Lake Victoria in northwestern Tanzania, serving as the capital of the Kagera Region and a local transport and trade hub.
-
C.
Nyamwezi
Nyamwezi is a Bantu language spoken primarily in northwestern Tanzania by the Nyamwezi people.
-
D.
Kalangala
Kalangala is a town on Uganda’s Ssese Islands in Lake Victoria, serving as the administrative and commercial center of Kalangala District.
-
E.
Kigoma Region
Kigoma Region is a western Tanzanian administrative region along Lake Tanganyika, known for its biodiversity and as a center for primate 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.