Triple
T24805944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runge phenomenon |
E620658
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | numerical analysis phenomenon |
C49568
|
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: numerical analysis phenomenon Context triple: [Runge phenomenon, instanceOf, numerical analysis phenomenon]
-
A.
phenomenon in analytic number theory
A phenomenon in analytic number theory is a recurring pattern or behavior in the distribution or properties of numbers—often primes or arithmetic functions—that is revealed and studied using tools from complex analysis and asymptotic methods.
-
B.
criterion in numerical analysis
A criterion in numerical analysis is a quantitative condition or rule—such as a tolerance, convergence test, or stopping condition—used to assess the accuracy, stability, or termination of an algorithm or computational method.
-
C.
numerical stability condition
A numerical stability condition is a mathematical requirement on the step size, discretization parameters, or algorithmic choices that ensures errors in a numerical method do not grow uncontrollably during computation.
-
D.
example in mathematical analysis
An example in mathematical analysis is a specific function, sequence, or construction used to illustrate, test, or clarify a general concept, theorem, or phenomenon within the subject.
-
E.
pathological function in analysis
A pathological function in analysis is a deliberately constructed example that defies typical intuitions—often satisfying formal definitions while exhibiting extreme or counterintuitive behavior, such as being continuous everywhere but differentiable nowhere.
- 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_69e2fabf26bc8190b191faac8f67065b |
completed | April 18, 2026, 3:30 a.m. |
Created at: April 18, 2026, 4:50 a.m.