Triple
T19871714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peugeot i-Cockpit digital cluster |
E477531
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | automotive human–machine interface component |
C31229
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: automotive human–machine interface component Context triple: [Peugeot i-Cockpit digital cluster, instanceOf, automotive human–machine interface component]
-
A.
in-vehicle infotainment platform
An in-vehicle infotainment platform is an integrated hardware and software system that delivers multimedia, navigation, connectivity, and vehicle information services through the car’s user interfaces.
-
B.
machine interface
chosen
A machine interface is the conceptual boundary and set of mechanisms through which humans or other systems interact with, control, and receive feedback from a machine.
-
C.
intelligent transportation system component
An intelligent transportation system component is a hardware or software element that collects, processes, or communicates transportation-related data to optimize traffic flow, enhance safety, and improve overall system efficiency.
-
D.
automotive architecture
Automotive architecture is the conceptual and structural design framework that defines how a vehicle’s key systems, components, and spatial layout are organized and integrated to meet performance, safety, and user experience goals.
-
E.
automotive safety system
An automotive safety system is an integrated set of components and technologies designed to prevent accidents or reduce injury and damage when collisions occur.
- F. None of above.
Provenance (1 batch)
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_69d8e51e7d948190aedbcd6c30361c39 |
completed | April 10, 2026, 11:55 a.m. |
Created at: April 10, 2026, 1:51 p.m.