Triple
T33923579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BAe Dominie T1 |
E869682
|
entity |
| Predicate | hasCabinLayout |
P5253
|
FINISHED |
| Object | side‑facing training consoles in main cabin |
—
|
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: side‑facing training consoles in main cabin | Statement: [BAe Dominie T1, hasCabinLayout, side‑facing training consoles in main cabin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCabinLayout Context triple: [BAe Dominie T1, hasCabinLayout, side‑facing training consoles in main cabin]
-
A.
cabinConfiguration
Indicates how the interior space of a vehicle, vessel, or aircraft is arranged and organized for occupants or cargo.
-
B.
intendedVehicleLayout
Indicates the planned or designed seating or interior configuration that a vehicle is meant to have.
-
C.
hasCabinClass
Indicates that an entity (such as a booking, ticket, or seat) is associated with a specific cabin class (e.g., economy, business, first).
-
D.
vehicleLayout
chosen
Indicates how the components or seating within a vehicle are arranged or configured relative to each other.
-
E.
vehicleLayoutCompatibility
Indicates that two vehicle-related components or systems are suitable to be arranged or integrated together within a given vehicle layout.
- 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_69f349992c508190aa4afa24a086cc8c |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff46afe7e481908f2862ed11c88db2 |
completed | May 9, 2026, 2:37 p.m. |
| PD | Predicate disambiguation | batch_69ff45e9151881909c444a655e852165 |
completed | May 9, 2026, 2:34 p.m. |
Created at: May 1, 2026, 1:49 a.m.