Triple
T19790468
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julius Nyerere International Airport |
E475394
|
entity |
| Predicate | servesAsHubFor |
P423
|
FINISHED |
| Object | Precision Air |
—
|
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: Precision Air | Statement: [Julius Nyerere International Airport, servesAsHubFor, Precision Air]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Precision Air Context triple: [Julius Nyerere International Airport, servesAsHubFor, Precision Air]
-
A.
Precision Air
chosen
Precision Air is a Tanzanian airline that operates regional and domestic flights within East Africa.
-
B.
Precisely
Precisely is a data integrity and location intelligence company known for providing software and services that help organizations ensure accurate, consistent, and contextualized data for analytics and decision-making.
-
C.
AirSWIFT
AirSWIFT is a Philippine regional airline known for operating domestic flights that connect Manila to popular island destinations such as El Nido and other tourist hubs.
-
D.
J-Air
J-Air is a Japanese regional airline operating domestic feeder and short-haul routes on behalf of Japan Airlines.
-
E.
Airflow
Airflow is an open-source platform for authoring, scheduling, and monitoring complex data workflows as directed acyclic graphs.
- 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e653c217bc819092c517b27ca22087 |
completed | April 20, 2026, 4:26 p.m. |
Created at: April 10, 2026, 1:49 p.m.