Triple
T3854420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nadi–Los Angeles |
E85376
|
entity |
| Predicate | flightCategory |
P18239
|
FINISHED |
| Object | intercontinental route |
—
|
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: intercontinental route | Statement: [Nadi–Los Angeles, flightCategory, intercontinental route]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: flightCategory Context triple: [Nadi–Los Angeles, flightCategory, intercontinental route]
-
A.
airlineCategory
Indicates the classification or type of an airline within a defined categorization system (e.g., full-service, low-cost, regional).
-
B.
hasTypeOfFlights
chosen
Indicates that an entity offers, includes, or is associated with specific categories or kinds of flights.
-
C.
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).
-
D.
typicalAircraftTypeCategory
Indicates the general class or category of aircraft type that is most commonly associated with or used in a given context.
-
E.
aircraftType
Indicates the specific model or category of aircraft associated with an entity or event.
- 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_69aed936de1c81908f91bed80f70abb2 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec0438308190865ff74bee5a1cf2 |
completed | March 9, 2026, 3:49 p.m. |
| PD | Predicate disambiguation | batch_69aee752c8a48190a670f73ed0bf1e61 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:19 p.m.