Triple
T5012947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carcassonne Airport |
E112668
|
entity |
| Predicate | hasLowCostCarrierOperations |
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: [Carcassonne Airport, hasLowCostCarrierOperations, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLowCostCarrierOperations Context triple: [Carcassonne Airport, hasLowCostCarrierOperations, 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.
operatesAsRegionalCarrierFor
Indicates that one transportation company functions as a regional carrier on behalf of another, typically providing local or feeder services under the larger company’s brand or network.
-
D.
hasAirlines
Indicates that one entity (such as an airport, city, or country) is served by or associated with one or more airline operators.
-
E.
operatesFeederFlightsFor
Indicates that one airline or carrier runs connecting or short-haul feeder flights on behalf of, or in support of, another airline’s main services.
- F. None of above. chosen
Provenance (4 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_69bd4434acb8819086679dbeccc2fe54 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd730f12a481908a27c15dc73987c6 |
completed | March 20, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69bd714cbc448190aa53a8a83d768b64 |
completed | March 20, 2026, 4:09 p.m. |
| PDg | Predicate description generation | batch_69bd73089f548190834103366e24ab40 |
completed | March 20, 2026, 4:17 p.m. |
Created at: March 20, 2026, 1:35 p.m.