Triple
T17521088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TensorFlow Estimators |
E426678
|
entity |
| Predicate | hasExampleImplementation |
P127768
|
FINISHED |
| Object | DNNLinearCombinedRegressor |
—
|
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: DNNLinearCombinedRegressor | Statement: [TensorFlow Estimators, hasExampleImplementation, DNNLinearCombinedRegressor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DNNLinearCombinedRegressor Context triple: [TensorFlow Estimators, hasExampleImplementation, DNNLinearCombinedRegressor]
-
A.
DNNLinearCombinedClassifier
DNNLinearCombinedClassifier is a TensorFlow Estimator that combines deep neural network and linear (wide) models into a single classifier for tasks like structured data prediction.
-
B.
LinearRegressor
LinearRegressor is a TensorFlow Estimator that implements linear regression models for predicting continuous values from input features.
-
C.
LinearClassifier
LinearClassifier is a TensorFlow Estimator that implements a linear model for classification tasks, typically using features combined with linear weights to predict discrete labels.
-
D.
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.
-
E.
TensorFlow Estimators
TensorFlow Estimators are a high-level TensorFlow API that simplifies building, training, and deploying machine learning models with standardized workflows and production-ready features.
- 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: DNNLinearCombinedRegressor Target entity description: DNNLinearCombinedRegressor is a TensorFlow Estimator that combines deep neural network and linear (wide) models to perform regression tasks.
-
A.
DNNLinearCombinedClassifier
DNNLinearCombinedClassifier is a TensorFlow Estimator that combines deep neural network and linear (wide) models into a single classifier for tasks like structured data prediction.
-
B.
LinearRegressor
LinearRegressor is a TensorFlow Estimator that implements linear regression models for predicting continuous values from input features.
-
C.
LinearClassifier
LinearClassifier is a TensorFlow Estimator that implements a linear model for classification tasks, typically using features combined with linear weights to predict discrete labels.
-
D.
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.
-
E.
TensorFlow Estimators
TensorFlow Estimators are a high-level TensorFlow API that simplifies building, training, and deploying machine learning models with standardized workflows and production-ready features.
- 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.