Triple
T23801752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nirenberg problem |
E588691
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | prescribed curvature problem |
C41097
|
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: prescribed curvature problem Context triple: [Nirenberg problem, instanceOf, prescribed curvature problem]
-
A.
curvature tensor
A curvature tensor is a multilinear mathematical object in differential geometry that measures how much a space (or manifold) deviates from being flat by quantifying the failure of vectors to return to their original direction after parallel transport around infinitesimal loops.
-
B.
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).
-
C.
problem in optimal transport theory
A problem in optimal transport theory seeks the most efficient way to move mass from one probability distribution to another while minimizing a given cost function.
-
D.
boundary value problem
chosen
A boundary value problem is a mathematical problem in which a differential equation is solved subject to specified conditions (boundary values) imposed on the solution at the boundaries of the domain.
-
E.
geometric optimization problem
A geometric optimization problem is a mathematical task that involves finding the best (e.g., shortest, largest, or most efficient) geometric configuration or measurement under given constraints.
- 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_69e25d15db58819092ac1e6791696fd9 |
completed | April 17, 2026, 4:17 p.m. |
Created at: April 17, 2026, 7:53 p.m.