Triple
T26243002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TrueCut Motion |
E656364
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | frame-optimization technology |
C9941
|
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: frame-optimization technology Context triple: [TrueCut Motion, instanceOf, frame-optimization technology]
-
A.
real-time rendering technology
chosen
Real-time rendering technology is a class of systems and algorithms that generate and display interactive, visually coherent images or scenes at high frame rates, typically for applications like games, simulations, and virtual reality.
-
B.
optical technology
Optical technology encompasses devices and systems that generate, manipulate, transmit, or detect light to enable applications such as imaging, communication, sensing, and information processing.
-
C.
latency reduction technology
Latency reduction technology encompasses methods and systems designed to minimize the delay between data transmission, processing, and response in digital networks and computing environments.
-
D.
motion picture technology patent
A motion picture technology patent is a legal protection granted for novel inventions, methods, or systems related to the creation, recording, processing, distribution, or display of moving images.
-
E.
dynamic frequency scaling technology
Dynamic frequency scaling technology automatically adjusts a processor’s operating frequency (and often voltage) in real time based on workload and thermal conditions to optimize power consumption and performance.
- 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, 9:04 p.m.