Triple

T17521002
Position Surface form Disambiguated ID Type / Status
Subject TensorFlow SavedModel (via conversion) E426677 entity
Predicate inputFormatOf P80182 FINISHED
Object TensorFlow.js Layers model format 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: TensorFlow.js Layers model format | Statement: [TensorFlow SavedModel (via conversion), inputFormatOf, TensorFlow.js Layers model format]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TensorFlow.js Layers model format
Context triple: [TensorFlow SavedModel (via conversion), inputFormatOf, TensorFlow.js Layers model format]
  • A. TensorFlow.js chosen
    TensorFlow.js is a JavaScript library that enables training and running machine learning models directly in the browser and in Node.js using TensorFlow.
  • B. TensorFlow Model Analysis
    TensorFlow Model Analysis is an open-source library for evaluating, validating, and monitoring machine learning models—especially at scale and on large datasets—within TensorFlow-based pipelines.
  • 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. 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. NNEF
    NNEF (Neural Network Exchange Format) is an open standard from the Khronos Group designed to enable portable, efficient interchange of trained neural network models across different hardware and software platforms.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: inputFormatOf
Context triple: [TensorFlow SavedModel (via conversion), inputFormatOf, TensorFlow.js Layers model format]
  • A. packageFormat
    Indicates the format or type in which a package is structured, encoded, or delivered.
  • B. definesFormatFor chosen
    Indicates that one entity specifies or establishes the format or structural pattern to be used by another entity.
  • C. operatesInFormat
    Indicates that an entity functions, performs its role, or is carried out using a specified format.
  • D. isPrimaryFormatFor
    Indicates that one format is the main or default format used to represent or distribute another related entity.
  • E. usedFormat
    Indicates that one entity employs or applies a particular format or representation in relation to another entity.
  • F. None of above.

Provenance (3 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.
Created at: April 10, 2026, 5:49 a.m.