Triple
T16249708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Banach–Steinhaus theorem |
E394468
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | uniform boundedness principle |
C15242
|
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: uniform boundedness principle Context triple: [Banach–Steinhaus theorem, instanceOf, uniform boundedness principle]
-
A.
criterion for uniform convergence
A criterion for uniform convergence is a condition or set of conditions that allows one to determine whether a sequence (or series) of functions converges uniformly to a limiting function on a given domain.
-
B.
functional analysis result
chosen
A functional analysis result is a formal conclusion or theorem that characterizes the behavior, structure, or properties of functions and operators on infinite-dimensional spaces, typically within the framework of normed, Banach, or Hilbert spaces.
-
C.
stability concept in functional equations
A stability concept in functional equations studies how small deviations from an exact functional relationship affect the existence and form of nearby exact solutions, typically quantifying when approximate solutions imply true solutions close in some specified sense.
-
D.
complete metric space
A complete metric space is a metric space in which every Cauchy sequence converges to a limit that lies within the space.
-
E.
norm inequality
A norm inequality is a mathematical statement that compares the sizes (norms) of vectors or functions, often establishing bounds or relationships between different norms in a vector space.
- 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
Created at: April 10, 2026, 5:04 a.m.