Triple
T36170383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Generalized method of moments |
E1046128
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | method of moments estimator |
C56235
|
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: method of moments estimator Context triple: [Generalized method of moments, instanceOf, method of moments estimator]
-
A.
statistical estimator
chosen
A statistical estimator is a rule or function that uses sample data to produce an approximate value (estimate) of an unknown population parameter.
-
B.
parameter estimation technique
A parameter estimation technique is a systematic method used to infer the numerical values of unknown model parameters from observed data, typically by optimizing a chosen error or likelihood criterion.
-
C.
composite estimator
A composite estimator is a statistical estimator formed by combining two or more individual estimators, often through weighted averaging, to improve overall accuracy, stability, or robustness of parameter estimates.
-
D.
redescending M-estimator
A redescending M-estimator is a robust statistical estimator whose influence function decreases back toward zero for large residuals, thereby downweighting extreme outliers more strongly than standard M-estimators.
-
E.
stochastic approximation method
A stochastic approximation method is an iterative algorithmic technique for finding roots or optima of functions when only noisy or sample-based observations are available, updating estimates using random data to converge to the desired solution.
- 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_69f76e396bc88190b99d221bff9be27a |
completed | May 3, 2026, 3:48 p.m. |
Created at: May 3, 2026, 4:08 p.m.