Triple
T6907482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GCXO |
E159846
|
entity |
| Predicate | hasSecondaryTrafficDirection |
P4069
|
FINISHED |
| Object | mainland Spain flights |
—
|
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: mainland Spain flights | Statement: [GCXO, hasSecondaryTrafficDirection, mainland Spain flights]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSecondaryTrafficDirection Context triple: [GCXO, hasSecondaryTrafficDirection, mainland Spain flights]
-
A.
hasTrafficDirection
chosen
Indicates that there is a specified flow or orientation of traffic associated with an entity (such as a road, lane, or route).
-
B.
hasPrimaryTrafficType
Indicates that an entity is associated with a main or predominant type of traffic it handles or is designed for.
-
C.
hasTwoOperationalDirections
Indicates that an entity supports or functions in two distinct operational directions or modes.
-
D.
hasTrafficPattern
Indicates that there is a characteristic or recurring flow of traffic associated with an entity, such as its typical volume, direction, or timing of movement.
-
E.
hasDirectionType
Indicates that something possesses or is associated with a specific type or category of direction.
- 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_69c68839ccb88190b4aa5cc1aca3448f |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6d9bd7b3c8190842eb83679c322d5 |
completed | March 27, 2026, 7:25 p.m. |
| PD | Predicate disambiguation | batch_69c6d7b93d688190a297244ce81b67ac |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:25 p.m.