Triple

T17520589
Position Surface form Disambiguated ID Type / Status
Subject StandardScaler E426669 entity
Predicate relatedTo P37 FINISHED
Object MinMaxScaler — 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: MinMaxScaler | Statement: [StandardScaler, relatedTo, MinMaxScaler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MinMaxScaler
Context triple: [StandardScaler, relatedTo, MinMaxScaler]
  • A. StandardScaler
    StandardScaler is a preprocessing tool in machine learning that normalizes numerical features by removing the mean and scaling to unit variance.
  • B. 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.
  • C. 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.
  • D. Batch Normalization
    Batch Normalization is a deep learning technique that stabilizes and accelerates neural network training by normalizing layer inputs using mini-batch statistics.
  • E. Tukey's biweight
    Tukey's biweight is a robust statistical estimator that downweights outliers to provide resistant measures of central tendency or regression fits.
  • 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: MinMaxScaler
Target entity description: MinMaxScaler is a data preprocessing tool that rescales numerical features to a specified range, typically [0, 1], to normalize input for machine learning models.
  • A. StandardScaler
    StandardScaler is a preprocessing tool in machine learning that normalizes numerical features by removing the mean and scaling to unit variance.
  • B. 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.
  • C. 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.
  • D. Batch Normalization
    Batch Normalization is a deep learning technique that stabilizes and accelerates neural network training by normalizing layer inputs using mini-batch statistics.
  • E. Tukey's biweight
    Tukey's biweight is a robust statistical estimator that downweights outliers to provide resistant measures of central tendency or regression fits.
  • 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.