Triple
T35863444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Attention Assist |
E1037016
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | drowsiness detection system |
C62038
|
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: drowsiness detection system Context triple: [Attention Assist, instanceOf, drowsiness detection system]
-
A.
sleep study
A sleep study is a medical evaluation that monitors a person's sleep patterns, breathing, brain activity, and other physiological functions overnight to diagnose sleep disorders.
-
B.
cooperative surveillance system
A cooperative surveillance system is a network of distributed sensors, platforms, and agents that share and fuse information in real time to achieve more comprehensive, accurate, and resilient monitoring than any individual component could provide alone.
-
C.
retina-inspired sensor
A retina-inspired sensor is a bio-mimetic imaging device that emulates the structure and processing principles of the human retina to capture and pre-process visual information efficiently and adaptively.
-
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.
infrared surveillance system
An infrared surveillance system is a security solution that uses infrared sensors and cameras to detect, monitor, and record heat signatures in low-light or no-light environments for continuous observation and threat detection.
- F. None of above. chosen
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_69f76e1d279c8190843e5b64a0a12c3f |
completed | May 3, 2026, 3:47 p.m. |
Created at: May 3, 2026, 4:06 p.m.