Triple

T18704834
Position Surface form Disambiguated ID Type / Status
Subject ExampleGen E457342 entity
Predicate supportsFormat P203 FINISHED
Object TFRecord 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: TFRecord | Statement: [ExampleGen, supportsFormat, TFRecord]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TFRecord
Context triple: [ExampleGen, supportsFormat, TFRecord]
  • A. tf.data API
    The tf.data API is a TensorFlow library for building efficient, scalable input pipelines that load, preprocess, and feed data into machine learning models.
  • B. TensorFlow I/O
    TensorFlow I/O is an extension library for TensorFlow that provides specialized input/output operations and dataset integrations for a wide range of file formats and data sources beyond the core framework’s built-in support.
  • 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 Transform
    TensorFlow Transform is a TensorFlow-based library for performing scalable, full-pass data preprocessing and feature engineering that can be applied consistently in both training and serving.
  • E. TensorFlow Datasets
    TensorFlow Datasets is a collection of ready-to-use, standardized datasets for machine learning and deep learning workflows in TensorFlow and other frameworks.
  • 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: TFRecord
Target entity description: TFRecord is TensorFlow’s native binary file format designed for efficient storage and streaming of large-scale structured data, especially for machine learning pipelines.
  • A. tf.data API
    The tf.data API is a TensorFlow library for building efficient, scalable input pipelines that load, preprocess, and feed data into machine learning models.
  • B. TensorFlow I/O
    TensorFlow I/O is an extension library for TensorFlow that provides specialized input/output operations and dataset integrations for a wide range of file formats and data sources beyond the core framework’s built-in support.
  • 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 Transform
    TensorFlow Transform is a TensorFlow-based library for performing scalable, full-pass data preprocessing and feature engineering that can be applied consistently in both training and serving.
  • E. TensorFlow Datasets
    TensorFlow Datasets is a collection of ready-to-use, standardized datasets for machine learning and deep learning workflows in TensorFlow and other frameworks.
  • 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_69d8d392aad081909fe31aa03e6e97d1 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5671665bc8190b9b4a4ce4ec5b2eb completed April 19, 2026, 11:36 p.m.
Created at: April 10, 2026, 11:49 a.m.