Triple
T13040449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Embraer 170 |
E327176
|
entity |
| Predicate | typicalSeatLayout |
P16826
|
FINISHED |
| Object | 2-2 economy class |
—
|
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: 2-2 economy class | Statement: [Embraer 170, typicalSeatLayout, 2-2 economy class]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSeatLayout Context triple: [Embraer 170, typicalSeatLayout, 2-2 economy class]
-
A.
individualSeats
Indicates that an entity provides or consists of separate, single-person seating positions rather than shared or bench-style seating.
-
B.
typicalSeat
Indicates the usual or standard seating position or location associated with an entity in a given context.
-
C.
seatingConfiguration
chosen
Indicates how seats are arranged or organized relative to each other in a given context.
-
D.
classesOfSeats
Indicates the different categories or types of seats associated with something, such as a venue, vehicle, or event.
-
E.
aircraftSeatingCategory
Indicates the classification of an aircraft’s seating arrangement or capacity type associated with an entity.
- 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d98a9829b48190b23624b6b3df4600 |
completed | April 10, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69d9803aca4c8190b1015cd159cc47a9 |
completed | April 10, 2026, 10:56 p.m. |
Created at: April 9, 2026, 8:55 p.m.