Triple
T810632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belfast International Airport |
E17535
|
entity |
| Predicate | passengerTrafficRole |
P7949
|
FINISHED |
| Object | major passenger airport |
—
|
LITERAL 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: major passenger airport | Statement: [Belfast International Airport, passengerTrafficRole, major passenger airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerTrafficRole Context triple: [Belfast International Airport, passengerTrafficRole, major passenger airport]
-
A.
hasPassengerRole
Indicates that an entity participates in a context or event specifically in the capacity or role of a passenger.
-
B.
transportationRole
Indicates a role or function that an entity has specifically in the context of providing, operating, or supporting transportation.
-
C.
passengerTraffic
Indicates the flow or volume of passengers moving through or using a particular transport service, route, or facility.
-
D.
transportRole
chosen
Indicates that an entity participates in a transportation process with a specific functional role (e.g., carrier, passenger, cargo, or operator).
-
E.
peakPassengerTrafficRank
Indicates the relative position of an entity in an ordered list based on the amount of passenger traffic it experiences at its peak.
- F. None of above.
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_69a4937ae8a08190b5084a03d532b30e |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab4c7418819085cb64c6bf5fa70c |
completed | March 1, 2026, 9:10 p.m. |
| PD | Predicate disambiguation | batch_69a4aa73df08819096d0553a4b2509de |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.