Triple
T17521087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TensorFlow Estimators |
E426678
|
entity |
| Predicate | hasExampleImplementation |
P127768
|
FINISHED |
| Object | DNNLinearCombinedClassifier |
—
|
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: DNNLinearCombinedClassifier | Statement: [TensorFlow Estimators, hasExampleImplementation, DNNLinearCombinedClassifier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DNNLinearCombinedClassifier Context triple: [TensorFlow Estimators, hasExampleImplementation, DNNLinearCombinedClassifier]
-
A.
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.
-
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.
Tensor2Tensor library
Tensor2Tensor library is an open-source deep learning toolkit from Google designed to simplify training and sharing state-of-the-art neural network models, particularly for sequence-to-sequence tasks like machine translation.
-
D.
TensorFlow Serving
TensorFlow Serving is a flexible, high-performance system for deploying and serving machine learning models in production, particularly those built with TensorFlow.
-
E.
DSSM
DSSM is the post-nominal abbreviation used by recipients of the U.S. Defense Superior Service Medal, a high-level military decoration awarded for superior meritorious service in a position of significant responsibility.
- 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: DNNLinearCombinedClassifier Target entity description: DNNLinearCombinedClassifier is a TensorFlow Estimator that combines deep neural network and linear (wide) models into a single classifier for tasks like structured data prediction.
-
A.
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.
-
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.
Tensor2Tensor library
Tensor2Tensor library is an open-source deep learning toolkit from Google designed to simplify training and sharing state-of-the-art neural network models, particularly for sequence-to-sequence tasks like machine translation.
-
D.
TensorFlow Serving
TensorFlow Serving is a flexible, high-performance system for deploying and serving machine learning models in production, particularly those built with TensorFlow.
-
E.
DSSM
DSSM is the post-nominal abbreviation used by recipients of the U.S. Defense Superior Service Medal, a high-level military decoration awarded for superior meritorious service in a position of significant responsibility.
- 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.