Triple
T4040439
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saab 340 |
E83931
|
entity |
| Predicate | typicalSeatingLayout |
P16826
|
FINISHED |
| Object | 1‑2 abreast |
—
|
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: 1‑2 abreast | Statement: [Saab 340, typicalSeatingLayout, 1‑2 abreast]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSeatingLayout Context triple: [Saab 340, typicalSeatingLayout, 1‑2 abreast]
-
A.
seatingConfiguration
chosen
Indicates how seats are arranged or organized relative to each other in a given context.
-
B.
typicalSeat
Indicates the usual or standard seating position or location associated with an entity in a given context.
-
C.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
-
D.
seatingPosition
Indicates the relative location or arrangement of an entity’s seat with respect to other seats or a reference point in a seating layout.
-
E.
numberOfSeatingRows
Indicates the total count of seating rows associated with an entity, such as a venue, vehicle, or seating area.
- 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_69aed92f7cf0819098e0539bdcc3767f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb39400881909d0f5430f04e441c |
completed | March 9, 2026, 4:54 p.m. |
| PD | Predicate disambiguation | batch_69aef900386481909d04555a9ec9b0e3 |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:37 p.m.