Triple
T4277146
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LogisticRegression |
E97070
|
entity |
| Predicate | module |
P29649
|
FINISHED |
| Object | sklearn.linear_model |
E17661
|
NE FINISHED |
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: sklearn.linear_model | Statement: [LogisticRegression, module, sklearn.linear_model]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: sklearn.linear_model Context triple: [LogisticRegression, module, sklearn.linear_model]
-
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.
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.
-
C.
Bayesian linear regression
Bayesian linear regression is a statistical modeling approach that treats regression coefficients and predictions probabilistically by placing prior distributions on parameters and updating them with observed data.
-
D.
Linear Estimation
Linear Estimation is a foundational text in signal processing and control theory that systematically develops the theory and applications of optimal estimation, including Kalman filtering and related methods.
-
E.
method of least squares
The method of least squares is a fundamental mathematical technique for estimating unknown parameters by minimizing the sum of squared differences between observed and predicted values, widely used in statistics, data fitting, and regression analysis.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501ef1388190b0c968b069014a59 |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5b7b3b52c8190ae7c05448faf5558 |
completed | March 14, 2026, 7:32 p.m. |
Created at: March 12, 2026, 11:07 p.m.