Triple
T37980500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christoffel–Minkowski problem |
E947537
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | problem in convex geometry |
C66431
|
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: problem in convex geometry Context triple: [Christoffel–Minkowski problem, instanceOf, problem in convex geometry]
-
A.
result in convex analysis
In convex analysis, a result is a formally stated and proven fact—such as a theorem, lemma, or proposition—that characterizes properties or relationships of convex sets, convex functions, or related optimization structures.
-
B.
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.
-
C.
problem in differential geometry
A problem in differential geometry involves analyzing and solving questions about the properties of curves, surfaces, and higher-dimensional manifolds using tools from calculus, linear algebra, and topology.
-
D.
classical geometry problem
A classical geometry problem is a mathematical question involving shapes, sizes, relative positions, and properties of figures, typically solvable using traditional Euclidean methods and constructions.
-
E.
research program in geometry
A research program in geometry is a coordinated, long-term investigation that develops and applies geometric concepts, methods, and conjectures to systematically explore and solve interconnected mathematical problems.
- 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_69f76ef8a1d08190a741bbbc5970e3b3 |
completed | May 3, 2026, 3:51 p.m. |
Created at: May 3, 2026, 4:20 p.m.