Triple
T18300502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ray |
E438345
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | scikit-learn |
—
|
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: scikit-learn | Statement: [Ray, integratesWith, scikit-learn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: scikit-learn Context triple: [Ray, integratesWith, scikit-learn]
-
A.
scikit-learn
chosen
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
-
B.
libsvm
libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
-
C.
LIBLINEAR
LIBLINEAR is an open-source machine learning library specialized for large-scale linear classification and regression, particularly efficient for linear SVMs and logistic regression.
-
D.
RandomForestClassifier
RandomForestClassifier is a popular ensemble machine learning algorithm in scikit-learn that builds multiple decision trees and aggregates their predictions for robust classification.
-
E.
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.
- 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_69d8b915e3e881909125d760c15d0c29 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5017e88cc8190a969eb628ca1b496 |
completed | April 19, 2026, 4:23 p.m. |
Created at: April 10, 2026, 10:35 a.m.