Triple
T36469215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arzelà–Ascoli theorem |
E898497
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | theorem in mathematical analysis |
C716
|
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: theorem in mathematical analysis Context triple: [Arzelà–Ascoli theorem, instanceOf, theorem in mathematical analysis]
-
A.
mathematical theorem
chosen
A mathematical theorem is a rigorously proven statement derived from axioms and previously established results, expressing a fundamental truth within a formal mathematical system.
-
B.
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.
-
C.
theory in real analysis
A theory in real analysis is a coherent framework of definitions, axioms, and theorems that rigorously describes and explains properties and behaviors of real numbers, sequences, functions, and related structures.
-
D.
functional analysis result
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.
-
E.
equation in functional analysis
An equation in functional analysis is a relation, typically involving functions and operators on infinite-dimensional spaces, that specifies conditions these objects must satisfy, often to study existence, uniqueness, and properties of solutions.
- 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_69f76e58ebd88190b75d9b169b59d793 |
completed | May 3, 2026, 3:48 p.m. |
Created at: May 3, 2026, 4:10 p.m.