Triple
T6281330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iberia’s Latin American network |
E140787
|
entity |
| Predicate | typicalFlightType |
P18239
|
FINISHED |
| Object | nonstop long-haul |
—
|
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: nonstop long-haul | Statement: [Iberia’s Latin American network, typicalFlightType, nonstop long-haul]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalFlightType Context triple: [Iberia’s Latin American network, typicalFlightType, nonstop long-haul]
-
A.
airlineType
Indicates the classification or category of an airline based on its operational or service characteristics.
-
B.
flightRegime
Indicates the operational conditions or phase of flight under which an aircraft or aerospace vehicle is functioning (e.g., speed, altitude, and atmospheric regime).
-
C.
hasTypeOfFlights
chosen
Indicates that an entity offers, includes, or is associated with specific categories or kinds of flights.
-
D.
typicalAircraftTypeCategory
Indicates the general class or category of aircraft type that is most commonly associated with or used in a given context.
-
E.
servesPassengerTrafficType
Indicates that a transportation facility or service accommodates a specified type or category of passenger traffic.
- 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_69c008cd17c8819082b82d3fbeb68047 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063dee62881908347283f16dcbe68 |
completed | March 22, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69c05608a5608190b22a1fdc4060470d |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:26 p.m.