Triple
T18817697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ACSIS |
E460180
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | digital autocorrelation spectrometer system |
C15549
|
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: digital autocorrelation spectrometer system Context triple: [ACSIS, instanceOf, digital autocorrelation spectrometer system]
-
A.
Raman spectrometer
A Raman spectrometer is an analytical instrument that uses monochromatic light to measure inelastically scattered photons from a sample, providing molecular vibrational information for chemical identification and characterization.
-
B.
spectroscopic instrument
chosen
A spectroscopic instrument is a device that disperses electromagnetic radiation into its component wavelengths and measures their intensities to determine the physical or chemical properties of a sample.
-
C.
fluorescence spectrometer
A fluorescence spectrometer is an analytical instrument that measures the intensity and wavelength distribution of fluorescent light emitted by a sample after excitation with a specific light source, enabling qualitative and quantitative analysis of molecular species.
-
D.
magnetic spectrometer system
A magnetic spectrometer system is an instrument that uses magnetic fields to separate and analyze charged particles or ions based on their momentum-to-charge ratio, enabling precise measurement of their energies and identities.
-
E.
spectrograph
A spectrograph is an instrument that disperses incoming light or other electromagnetic radiation into its component wavelengths to record and analyze their spectrum.
- 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_69d8dcf94c288190a06dea029ae4b223 |
completed | April 10, 2026, 11:20 a.m. |
Created at: April 10, 2026, 11:55 a.m.