Triple
T7019409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Precision Air |
E162779
|
entity |
| Predicate | cityServed |
P82
|
FINISHED |
| Object | Mwanza |
E43851
|
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: Mwanza | Statement: [Precision Air, cityServed, Mwanza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mwanza Context triple: [Precision Air, cityServed, Mwanza]
-
A.
Mwanza
chosen
Mwanza is a major port city in northwestern Tanzania, situated on the southern shores of Lake Victoria and serving as a key commercial and transport hub for the region.
-
B.
Mbanika
Mbanika is one of the main islands in the Russell Islands group in the Central Province of the Solomon Islands, known for its World War II history and natural tropical environment.
-
C.
Ntumu
Ntumu is a dialect of the Fang language spoken by Fang communities in parts of Central Africa, particularly in Equatorial Guinea, Gabon, and Cameroon.
-
D.
Unua
Unua is an Oceanic language spoken by a small community in Vanuatu.
-
E.
Ngundu
Ngundu is a small settlement in southern Zimbabwe that serves as a roadside stop and trading center along major routes between Harare and Beitbridge.
- 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_69c775707e30819088b311a1a87eee79 |
completed | March 28, 2026, 6:30 a.m. |
Created at: March 27, 2026, 2:34 p.m.