Triple
T26242103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lattice mVision |
E656343
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | edge AI and embedded vision platform |
C26073
|
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: edge AI and embedded vision platform Context triple: [Lattice mVision, instanceOf, edge AI and embedded vision platform]
-
A.
embedded computing platform series
A series of embedded computing platforms is a family of related hardware and software modules designed to provide scalable, application-specific processing, connectivity, and I/O capabilities for integration into dedicated electronic systems.
-
B.
embedded software platform
An embedded software platform is an integrated collection of software components, tools, and runtime services that provide a standardized environment for developing, deploying, and managing applications on resource-constrained embedded devices.
-
C.
automotive computing platform
An automotive computing platform is an integrated hardware and software system within a vehicle that manages and coordinates functions such as infotainment, driver assistance, connectivity, and vehicle control in a secure and real-time manner.
-
D.
embedded system
chosen
An embedded system is a specialized computing system that is dedicated to performing specific functions within a larger mechanical or electrical system, often with real-time computing constraints and limited resources.
-
E.
AI inference server
An AI inference server is a system that hosts trained machine learning models and processes incoming requests to generate predictions or responses in real time.
- 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_69ee5b4c59a881909d9ee4fd013fffd5 |
completed | April 26, 2026, 6:37 p.m. |
Created at: April 26, 2026, 9:03 p.m.