Triple
T13472012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Tennent Airport |
E311650
|
entity |
| Predicate | servedTypeOfTraffic |
P59443
|
FINISHED |
| Object | civil aviation |
—
|
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: civil aviation | Statement: [William Tennent Airport, servedTypeOfTraffic, civil aviation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servedTypeOfTraffic Context triple: [William Tennent Airport, servedTypeOfTraffic, civil aviation]
-
A.
servesPassengerTrafficType
Indicates that a transportation facility or service accommodates a specified type or category of passenger traffic.
-
B.
trafficType
Indicates the category or nature of traffic involved in a given interaction, flow, or connection (e.g., type of network, data, or transport traffic).
-
C.
originalTrafficType
Indicates the initial category or source classification of traffic before any changes, redirects, or reattributions occur.
-
D.
hasCargoTrafficType
Indicates that an entity is associated with a specific type or category of cargo traffic it handles or supports.
-
E.
hasPrimaryTrafficType
chosen
Indicates that an entity is associated with a main or predominant type of traffic it handles or is designed for.
- 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_69d806a938b8819097ec43a2229fc7f9 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf22e5f88190b1078f006c8ef7c0 |
completed | April 12, 2026, 2:41 p.m. |
| PD | Predicate disambiguation | batch_69dbadfddefc81909ef7fde23b181b5c |
completed | April 12, 2026, 2:36 p.m. |
Created at: April 9, 2026, 9:42 p.m.