Triple
T16420337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DS Active Scan Suspension |
E398799
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | camera-based suspension system |
C8156
|
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: camera-based suspension system Context triple: [DS Active Scan Suspension, instanceOf, camera-based suspension system]
-
A.
adaptive suspension system
chosen
An adaptive suspension system is a vehicle suspension technology that continuously adjusts damping and stiffness in real time based on driving conditions, road surface, and driver inputs to optimize comfort, handling, and stability.
-
B.
automotive suspension system
An automotive suspension system is the integrated assembly of springs, dampers, linkages, and related components that connects a vehicle’s body to its wheels to control ride comfort, handling, and road shock isolation.
-
C.
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.
-
D.
autonomous navigation system
An autonomous navigation system is a self-directed control framework that enables vehicles or robots to perceive their environment, plan routes, and move safely to a destination without human intervention.
-
E.
active head restraint system
An active head restraint system is a safety feature in vehicle seats that automatically moves the headrest forward and/or upward during a rear-end collision to reduce the risk of whiplash injuries.
- 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
Created at: April 10, 2026, 5:09 a.m.