Triple
T11632007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bellingham International Airport |
E276419
|
entity |
| Predicate | servesLowCostCarrierTraffic |
P60835
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Bellingham International Airport, servesLowCostCarrierTraffic, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesLowCostCarrierTraffic Context triple: [Bellingham International Airport, servesLowCostCarrierTraffic, yes]
-
A.
hasLowCostCarrierFocus
Indicates that the subject is primarily oriented toward or strategically focused on serving low-cost carrier operations.
-
B.
lowCostTerminal
Indicates that the terminal operates or is provided at a relatively low financial cost compared to alternatives.
-
C.
hasLowCostCarrierOperations
chosen
Indicates that an entity operates or is served by low-cost (budget) airline services.
-
D.
servesAirlinesFrom
Indicates that a location or facility provides service or operations for flights belonging to specific airlines originating from that place.
-
E.
servesAirlineType
Indicates that a service provider (such as an airport, terminal, or facility) accommodates or operates flights for a specified type or category of airline.
- 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_69d6aafa51148190ab84940694c00235 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a25aa9188190ab13d79139f37e7e |
completed | April 10, 2026, 7:10 a.m. |
| PD | Predicate disambiguation | batch_69d85dd94bdc819091fa2ed33eb31624 |
completed | April 10, 2026, 2:18 a.m. |
Created at: April 8, 2026, 9:39 p.m.