Triple
T31575635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GLEX |
E805689
|
entity |
| Predicate | associatedWithCabinClass |
P3037
|
FINISHED |
| Object | large-cabin business jet |
—
|
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: large-cabin business jet | Statement: [GLEX, associatedWithCabinClass, large-cabin business jet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithCabinClass Context triple: [GLEX, associatedWithCabinClass, large-cabin business jet]
-
A.
hasCabinClass
chosen
Indicates that an entity (such as a booking, ticket, or seat) is associated with a specific cabin class (e.g., economy, business, first).
-
B.
servesCabinClass
Indicates that a service provider (such as an airline or flight) offers or is available to a specified cabin class (e.g., economy, business, first).
-
C.
cabinClassAbove
Indicates that one cabin class is ranked higher or more premium than another in a class hierarchy.
-
D.
seatClass
Indicates the travel or seating category assigned to a passenger or seat (e.g., economy, business, first class).
-
E.
hasChangingCabins
Indicates that something provides or includes designated cabins or rooms intended for changing clothes.
- 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_69f348d3a86c8190a3e5e539a4dd125f |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a037c876524819098545e6037d3107d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e7aa0c8190bdc9ee4d54fc821b |
completed | May 12, 2026, 7:05 p.m. |
Created at: April 30, 2026, 10:21 p.m.