Triple
T17520632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pipeline (scikit-learn) |
E426670
|
entity |
| Predicate | usedWith |
P4791
|
FINISHED |
| Object | OneHotEncoder |
—
|
NE NERFINISHED |
How this triple was built (2 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: OneHotEncoder | Statement: [Pipeline (scikit-learn), usedWith, OneHotEncoder]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OneHotEncoder Context triple: [Pipeline (scikit-learn), usedWith, OneHotEncoder]
-
A.
OneHotEncoder
chosen
OneHotEncoder is a preprocessing tool in machine learning that converts categorical variables into a binary (one-hot) numeric format suitable for model training.
-
B.
ColumnTransformer
ColumnTransformer is a scikit-learn utility that applies different preprocessing or transformation pipelines to specified columns of a dataset within a single unified estimator.
-
C.
LogisticRegression
LogisticRegression is a scikit-learn machine learning estimator that models the probability of class membership using a linear decision boundary with logistic (sigmoid) or related link functions.
-
D.
TensorFlow Transform
TensorFlow Transform is a TensorFlow-based library for performing scalable, full-pass data preprocessing and feature engineering that can be applied consistently in both training and serving.
-
E.
EncoderDecoderModel
EncoderDecoderModel is a Hugging Face Transformers architecture that combines a separate encoder and decoder into a unified sequence-to-sequence model for tasks like translation, summarization, and text generation.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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.