Triple
T17520590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | StandardScaler |
E426669
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | RobustScaler |
—
|
NE NERFINISHED |
How this triple was built (3 steps)
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.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: RobustScaler | Statement: [StandardScaler, relatedTo, RobustScaler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RobustScaler Context triple: [StandardScaler, relatedTo, RobustScaler]
-
A.
StandardScaler
StandardScaler is a preprocessing tool in machine learning that normalizes numerical features by removing the mean and scaling to unit variance.
-
B.
Box–Cox transformation
The Box–Cox transformation is a family of power transformations used in statistics to stabilize variance and make data more normally distributed for modeling and analysis.
-
C.
Huber M-estimator
The Huber M-estimator is a robust statistical estimator of location that combines properties of the mean and median by using a loss function that is quadratic for small residuals and linear for large ones to reduce the influence of outliers.
-
D.
Tukey's biweight
Tukey's biweight is a robust statistical estimator that downweights outliers to provide resistant measures of central tendency or regression fits.
-
E.
Instance Normalization
Instance Normalization is a neural network normalization technique that normalizes each individual sample and channel independently, commonly used in tasks like style transfer to stabilize training and control feature statistics.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: RobustScaler Target entity description: RobustScaler is a data preprocessing tool in machine learning that scales features using statistics robust to outliers, such as the median and interquartile range.
-
A.
StandardScaler
StandardScaler is a preprocessing tool in machine learning that normalizes numerical features by removing the mean and scaling to unit variance.
-
B.
Box–Cox transformation
The Box–Cox transformation is a family of power transformations used in statistics to stabilize variance and make data more normally distributed for modeling and analysis.
-
C.
Huber M-estimator
The Huber M-estimator is a robust statistical estimator of location that combines properties of the mean and median by using a loss function that is quadratic for small residuals and linear for large ones to reduce the influence of outliers.
-
D.
Tukey's biweight
Tukey's biweight is a robust statistical estimator that downweights outliers to provide resistant measures of central tendency or regression fits.
-
E.
Instance Normalization
Instance Normalization is a neural network normalization technique that normalizes each individual sample and channel independently, commonly used in tasks like style transfer to stabilize training and control feature statistics.
- F. None of above. chosen
Provenance (2 batches)
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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d23cf08190925510344fa36f57 |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.