Triple
T12898250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nok Air |
E308548
|
entity |
| Predicate | aircraftLiveryFeature |
P17744
|
FINISHED |
| Object | bird beak painted on nose |
—
|
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: bird beak painted on nose | Statement: [Nok Air, aircraftLiveryFeature, bird beak painted on nose]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aircraftLiveryFeature Context triple: [Nok Air, aircraftLiveryFeature, bird beak painted on nose]
-
A.
liveryFeature
chosen
Indicates a characteristic or design element that is part of a specific livery or external appearance scheme.
-
B.
hasLivery
Indicates that one entity bears or displays the distinctive colors, markings, or branding (livery) associated with another entity.
-
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.
aircraftMarking
Indicates a relationship where a marking, symbol, or identifier is applied to or displayed on an aircraft.
-
E.
associatedWithAircraftColor
Indicates that there is a relationship between an aircraft and a specific color with which that aircraft is identified or depicted.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9717f3fc48190b61c8f6f36cd0725 |
completed | April 10, 2026, 9:54 p.m. |
| PD | Predicate disambiguation | batch_69d96fa776648190b9b5c30722ea50b6 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:40 p.m.