Triple
T8926754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neyman–Pearson theory of hypothesis testing |
E212555
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | hypothesis testing framework |
C9120
|
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: hypothesis testing framework Context triple: [Neyman–Pearson theory of hypothesis testing, instanceOf, hypothesis testing framework]
-
A.
statistical framework
chosen
A statistical framework is a structured set of principles, assumptions, and methods that guides how data are collected, modeled, analyzed, and interpreted to draw valid inferences about underlying phenomena.
-
B.
scientific hypothesis
A scientific hypothesis is a testable, falsifiable, and specific proposed explanation for an observed phenomenon that guides empirical investigation.
-
C.
behavior-driven development framework
A behavior-driven development framework is a software toolset that supports specifying, executing, and validating system behavior in a human-readable, example-driven format that bridges communication between business stakeholders and developers.
-
D.
scientific heuristic
A scientific heuristic is a practical, experience-based rule or strategy that guides researchers in generating hypotheses, designing experiments, or interpreting data without guaranteeing an optimal or strictly logical solution.
-
E.
scaling framework
A scaling framework is a structured approach that defines the principles, processes, and tools needed to grow a system, organization, or product efficiently and sustainably as demand increases.
- 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_69ca839481d48190b42b037e0d0f636c |
completed | March 30, 2026, 2:07 p.m. |
Created at: March 30, 2026, 6:57 p.m.