Triple

T18204417
Position Surface form Disambiguated ID Type / Status
Subject T5 E435867 entity
Predicate frameworkImplementation P130212 FINISHED
Object TensorFlow 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 | Statement: [T5, frameworkImplementation, TensorFlow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TensorFlow
Context triple: [T5, frameworkImplementation, TensorFlow]
  • A. TensorFlow chosen
    TensorFlow is an open-source, end-to-end machine learning and deep learning framework widely used for building, training, and deploying neural network models at scale.
  • B. TensorFlow ecosystem
    The TensorFlow ecosystem is a comprehensive suite of tools, libraries, and extensions built around the TensorFlow machine learning framework to support model development, training, deployment, and visualization.
  • C. Keras
    Keras is a high-level neural networks API written in Python that simplifies building, training, and deploying deep learning models, often running on top of frameworks like TensorFlow.
  • D. TensorFlow Extended
    TensorFlow Extended (TFX) is an end-to-end platform for deploying, managing, and scaling production machine learning pipelines built on TensorFlow.
  • E. TensorFlow.js
    TensorFlow.js is a JavaScript library that enables training and running machine learning models directly in the browser and in Node.js using TensorFlow.
  • 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: frameworkImplementation
Context triple: [T5, frameworkImplementation, TensorFlow]
  • A. frameworkSupport
    Indicates that one entity provides compatibility with, or operational support for, a particular framework used by another entity.
  • B. frameworkFor
    Indicates that one entity serves as a supporting structure, system, or basis that organizes, guides, or enables the development or functioning of another entity.
  • C. frameworkLayer
    Indicates a relationship where one framework is organized within, built upon, or conceptually assigned to a particular architectural or conceptual layer.
  • D. frameworkPresented
    Indicates that a particular framework has been formally introduced or shown to an audience or recipient.
  • E. frameworkName
    Indicates that a specific framework is identified by the given name.
  • 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_69d8b90dba6481908e119eb9aa4ca0cb completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4e222831081908f7d5500424e3acb completed April 19, 2026, 2:09 p.m.
PD Predicate disambiguation batch_69e4332155d88190b106d0dceb4554af completed April 19, 2026, 1:42 a.m.
PDg Predicate description generation batch_69e438f684e48190b38c64b58c518b6a completed April 19, 2026, 2:07 a.m.
Created at: April 10, 2026, 10:32 a.m.