Triple
T13894143
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hadamard’s example of ill-posed problems |
E334044
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | example in partial differential equations |
C17472
|
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: example in partial differential equations Context triple: [Hadamard’s example of ill-posed problems, instanceOf, example in partial differential equations]
-
A.
partial differential equation
A partial differential equation is an equation that relates the partial derivatives of an unknown multivariable function, describing how it changes with respect to several independent variables.
-
B.
result in partial differential equations
A result in partial differential equations is a proven statement or theorem that characterizes the existence, uniqueness, regularity, behavior, or qualitative properties of solutions to equations involving multivariable derivatives.
-
C.
example in mathematical analysis
chosen
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.
-
D.
result in mathematical physics
A result in mathematical physics is a rigorously proven statement that connects precise mathematical structures with physical theories, often clarifying, justifying, or predicting phenomena within a formal framework.
-
E.
equation in the calculus of variations
An equation in the calculus of variations is a mathematical relation, typically an Euler–Lagrange equation, that characterizes the functions making a given functional stationary (usually minimizing or maximizing its value).
- 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_69d81c5dd2d48190b7a5fc1e009de936 |
completed | April 9, 2026, 9:38 p.m. |
Created at: April 9, 2026, 10:15 p.m.