Triple
T6858896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wiener–Khinchin theorem |
E158222
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | theorem in signal processing |
C716
|
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: theorem in signal processing Context triple: [Wiener–Khinchin theorem, instanceOf, theorem in signal processing]
-
A.
tool in signal processing
A tool in signal processing is a conceptual or physical mechanism—such as an algorithm, filter, transform, or software module—used to analyze, modify, or extract information from signals.
-
B.
mathematical theorem
chosen
A mathematical theorem is a rigorously proven statement derived from axioms and previously established results, expressing a fundamental truth within a formal mathematical system.
-
C.
area of harmonic analysis
An area of harmonic analysis is a branch of mathematics focused on representing functions or signals as superpositions of basic waves and studying the properties of these representations.
-
D.
set of axioms in information theory
A set of axioms in information theory is a foundational collection of formal assumptions that precisely define and constrain measures of information, uncertainty, and related concepts so that theorems and results can be derived consistently.
-
E.
set of axioms in information theory
A set of axioms in information theory is a foundational collection of formal principles that precisely define and constrain measures of information, uncertainty, and related concepts so that consistent theorems and results can be derived.
- 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_69c68830cdbc8190a8301c7a9d9f651a |
completed | March 27, 2026, 1:37 p.m. |
Created at: March 27, 2026, 2:21 p.m.