Triple
T17587830
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heckman selection model |
E428370
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | limited information maximum likelihood model |
C26339
|
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: limited information maximum likelihood model Context triple: [Heckman selection model, instanceOf, limited information maximum likelihood model]
-
A.
statistical model
chosen
A statistical model is a mathematical representation of observed data and underlying random processes, used to describe relationships, make inferences, and generate predictions.
-
B.
large-scale model
A large-scale model is a computational model, often in machine learning or simulation, that operates with vast numbers of parameters or variables to capture complex patterns or behaviors across extensive datasets or systems.
-
C.
set of axioms in information theory
A set of axioms in information theory is a foundational collection of formal assumptions that precisely define and constrain measures of information, uncertainty, and related concepts so that theorems and results can be derived consistently.
-
D.
set of axioms in information theory
A set of axioms in information theory is a foundational collection of formal principles that precisely define and constrain measures of information, uncertainty, and related concepts so that consistent theorems and results can be derived.
-
E.
object in optimal stopping theory
An object in optimal stopping theory is an abstract entity (such as a stochastic process, payoff function, or stopping rule) whose evolution or evaluation over time determines when it is best to stop observing and take an action to maximize expected reward or minimize expected cost.
- 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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
Created at: April 10, 2026, 5:51 a.m.