Triple
T7329892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kapitza–Dirac effect |
E168971
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | diffraction effect |
C19514
|
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: diffraction effect Context triple: [Kapitza–Dirac effect, instanceOf, diffraction effect]
-
A.
diffraction pattern feature
chosen
A diffraction pattern feature is a distinct intensity variation or structural element within a diffraction pattern that reflects specific spatial arrangements, periodicities, or defects in the scattering object.
-
B.
photon correlation effect
Photon correlation effect is the phenomenon where statistical correlations between detected photons reveal underlying properties of a light source, such as coherence, quantum statistics, and emission dynamics.
-
C.
diffractive optical element
A diffractive optical element is a micro-structured optical component that manipulates light through diffraction to achieve functions such as beam shaping, splitting, or focusing.
-
D.
diffraction analysis method
A diffraction analysis method is a technique that interprets the pattern and intensity of waves scattered by a material to determine its structural, compositional, or physical properties.
-
E.
spectroscopic effect
A spectroscopic effect is any observable change or feature in a spectrum—such as shifts, splittings, intensity variations, or line broadenings—that arises from the interaction of electromagnetic radiation with matter and reveals information about a system’s structure, dynamics, or environment.
- 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_69c68a54cacc81908e3b773441f19566 |
completed | March 27, 2026, 1:47 p.m. |
Created at: March 27, 2026, 3:03 p.m.