Triple

T849723
Position Surface form Disambiguated ID Type / Status
Subject Keras E18356 entity
Predicate supportsBackend P15794 FINISHED
Object TensorFlow E17662 NE FINISHED

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: [Keras, supportsBackend, TensorFlow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TensorFlow
Context triple: [Keras, supportsBackend, 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. 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.
  • C. TensorFlow Extended
    TensorFlow Extended (TFX) is an end-to-end platform for deploying, managing, and scaling production machine learning pipelines built on TensorFlow.
  • D. 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.
  • E. Theano
    Theano is an open-source numerical computation library for Python that allows efficient definition, optimization, and evaluation of mathematical expressions, particularly those involving multi-dimensional arrays, and was widely used as a backend for deep learning frameworks.
  • 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: supportsBackend
Context triple: [Keras, supportsBackend, TensorFlow]
  • A. supportsFeature
    Indicates that one entity provides, enables, or is compatible with a particular feature or capability of another.
  • B. isSupportedBy
    Indicates that an entity is upheld, sustained, or enabled by another entity, which provides necessary assistance, resources, or justification.
  • C. supportsProduct
    Indicates that one entity provides assistance, compatibility, or necessary resources for the operation, use, or maintenance of a specified product.
  • D. supportsOperationsIn
    Indicates that one entity enables, facilitates, or backs the execution of operations within a specified context, area, or domain of another entity.
  • E. supportsUse chosen
    Indicates that one entity enables, allows, or is compatible with the use or operation of another entity.
  • F. None of above.

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_69a4938b04208190b82e1df6b572c548 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ac1fac3481909cba7070ce31a9b3 completed March 1, 2026, 9:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7c71702fc8190a143fe45b228ae24 completed March 4, 2026, 5:45 a.m.
PD Predicate disambiguation batch_69a4aa807adc8190ad808a573cf8e923 completed March 1, 2026, 9:07 p.m.
Created at: March 1, 2026, 7:38 p.m.