Triple
T6058163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Silicon Strip Detector |
E134965
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | semiconductor particle detector |
C2616
|
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: semiconductor particle detector Context triple: [Silicon Strip Detector, instanceOf, semiconductor particle detector]
-
A.
particle detector
chosen
A particle detector is a device or system that identifies, tracks, and measures properties of subatomic particles produced in physical processes or experiments.
-
B.
neutrino detector
A neutrino detector is a highly sensitive instrument or facility designed to observe and measure elusive neutrinos by capturing their rare interactions with matter, often deep underground or underwater to shield from background radiation.
-
C.
general-purpose detector
A general-purpose detector is a versatile sensing component that identifies and signals the presence, absence, or change of various physical or logical conditions across multiple domains or applications.
-
D.
circular particle accelerator
A circular particle accelerator is a device that uses magnetic fields to guide charged particles around a closed loop while electric fields repeatedly increase their energy for high-speed collisions or experiments.
-
E.
particle accelerator
A particle accelerator is a machine that uses electromagnetic fields to propel charged particles to high speeds and direct them into beams for research, medical, or industrial applications.
- 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_69c00877b6d4819096b0e163728b73a3 |
completed | March 22, 2026, 3:19 p.m. |
Created at: March 22, 2026, 4:10 p.m.