Triple

T17521083
Position Surface form Disambiguated ID Type / Status
Subject TensorFlow Estimators E426678 entity
Predicate hasExampleImplementation P127768 FINISHED
Object DNNClassifier NE NERFINISHED

How this triple was built (4 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: DNNClassifier | Statement: [TensorFlow Estimators, hasExampleImplementation, DNNClassifier]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: DNNClassifier
Context triple: [TensorFlow Estimators, hasExampleImplementation, DNNClassifier]
  • 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. TensorFlow Serving
    TensorFlow Serving is a flexible, high-performance system for deploying and serving machine learning models in production, particularly those built with TensorFlow.
  • D. 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.
  • E. LeNet
    LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
  • 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: DNNClassifier
Target entity description: DNNClassifier is a high-level TensorFlow Estimator for building and training deep neural network models for classification tasks.
  • A. TensorFlow Estimators chosen
    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. TensorFlow Serving
    TensorFlow Serving is a flexible, high-performance system for deploying and serving machine learning models in production, particularly those built with TensorFlow.
  • D. 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.
  • E. LeNet
    LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
  • F. None of above.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasExampleImplementation
Context triple: [TensorFlow Estimators, hasExampleImplementation, DNNClassifier]
  • A. hasExample
    Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
  • B. hasExampleType
    Indicates that something is associated with a specific type or category of example that characterizes or illustrates it.
  • C. hasStandardImplementation
    Indicates that an entity has a defined, commonly accepted implementation that follows an established standard or specification.
  • D. hasNotableImplementationAt
    Indicates that something has a significant or noteworthy implementation located at or associated with a particular place, context, or platform.
  • E. hasImplementationDetail
    Indicates that one entity specifies or contains a concrete implementation detail or internal mechanism related to another entity.
  • F. None of above. chosen

Provenance (4 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.
PD Predicate disambiguation batch_69e3b4f8b9888190aa8a45e09acf4319 completed April 18, 2026, 4:44 p.m.
PDg Predicate description generation batch_69e3bbb37d148190b7f38599c06594ee completed April 18, 2026, 5:13 p.m.
Created at: April 10, 2026, 5:49 a.m.