Triple
T17521085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TensorFlow Estimators |
E426678
|
entity |
| Predicate | hasExampleImplementation |
P127768
|
FINISHED |
| Object | LinearClassifier |
—
|
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: LinearClassifier | Statement: [TensorFlow Estimators, hasExampleImplementation, LinearClassifier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LinearClassifier Context triple: [TensorFlow Estimators, hasExampleImplementation, LinearClassifier]
-
A.
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.
-
B.
Naive Bayes classifier
A Naive Bayes classifier is a simple probabilistic machine learning model that applies Bayes’ theorem under strong independence assumptions between features to perform fast and effective classification.
-
C.
Perceptrons
Perceptrons is a seminal 1969 book by Marvin Minsky and Seymour Papert that critically analyzes the capabilities and limitations of early neural network models, profoundly influencing the development of artificial intelligence and machine learning.
-
D.
Fisher's linear discriminant
Fisher's linear discriminant is a classic statistical technique for dimensionality reduction and classification that projects data onto a line to maximize separation between classes.
-
E.
libsvm
libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
- 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: LinearClassifier Target entity description: LinearClassifier is a TensorFlow Estimator that implements a linear model for classification tasks, typically using features combined with linear weights to predict discrete labels.
-
A.
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.
-
B.
Naive Bayes classifier
A Naive Bayes classifier is a simple probabilistic machine learning model that applies Bayes’ theorem under strong independence assumptions between features to perform fast and effective classification.
-
C.
Perceptrons
Perceptrons is a seminal 1969 book by Marvin Minsky and Seymour Papert that critically analyzes the capabilities and limitations of early neural network models, profoundly influencing the development of artificial intelligence and machine learning.
-
D.
Fisher's linear discriminant
Fisher's linear discriminant is a classic statistical technique for dimensionality reduction and classification that projects data onto a line to maximize separation between classes.
-
E.
libsvm
libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
- 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.