Triple
T17573027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Airbus A320neo |
E427985
|
entity |
| Predicate | typicalTwoClassSeating |
P16826
|
FINISHED |
| Object | 150 to 180 passengers |
—
|
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: 150 to 180 passengers | Statement: [Airbus A320neo, typicalTwoClassSeating, 150 to 180 passengers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTwoClassSeating Context triple: [Airbus A320neo, typicalTwoClassSeating, 150 to 180 passengers]
-
A.
typicalSeat
Indicates the usual or standard seating position or location associated with an entity in a given context.
-
B.
seatingConfiguration
chosen
Indicates how seats are arranged or organized relative to each other in a given context.
-
C.
seatCategory
Indicates the classification or type of a seat (e.g., by comfort level, price tier, or section) assigned to an entity.
-
D.
classesOfSeats
Indicates the different categories or types of seats associated with something, such as a venue, vehicle, or event.
-
E.
hasSeatingClassification
Indicates that an entity is assigned a specific type or category of seating arrangement or capacity.
- 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_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e459330c788190907a02fc98e0e24b |
completed | April 19, 2026, 4:25 a.m. |
| PD | Predicate disambiguation | batch_69e3b4fd7d048190b54ee4c6155612a5 |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:50 a.m.