Triple
T17772763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SkyUp Airlines |
E443681
|
entity |
| Predicate | hasLiveryFeature |
P17744
|
FINISHED |
| Object | orange tail with SKYUP titles |
—
|
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: orange tail with SKYUP titles | Statement: [SkyUp Airlines, hasLiveryFeature, orange tail with SKYUP titles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLiveryFeature Context triple: [SkyUp Airlines, hasLiveryFeature, orange tail with SKYUP titles]
-
A.
hasLivery
Indicates that one entity bears or displays the distinctive colors, markings, or branding (livery) associated with another entity.
-
B.
liveryFeature
chosen
Indicates a characteristic or design element that is part of a specific livery or external appearance scheme.
-
C.
liveryColors
Indicates the specific set of colors used as the official or characteristic color scheme associated with an entity (such as a brand, organization, or vehicle).
-
D.
hasVehicleFeature
Indicates that a vehicle possesses, includes, or is equipped with a specific feature or characteristic.
-
E.
hasInsigniaComponent
Indicates that an entity includes, bears, or is composed of a particular insignia or emblem as one of its components.
- 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_69d8b9ef17708190bdf7e2adbf14ddc2 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4871a2130819081743ae89dddc64b |
completed | April 19, 2026, 7:41 a.m. |
| PD | Predicate disambiguation | batch_69e3d8d8e538819084f1584426b41d5e |
completed | April 18, 2026, 7:17 p.m. |
Created at: April 10, 2026, 10:12 a.m.