Triple

T4425385
Position Surface form Disambiguated ID Type / Status
Subject JAX E95194 entity
Predicate compatibleWith P203 FINISHED
Object TensorFlow Probability (JAX backend)
TensorFlow Probability (JAX backend) is a probabilistic programming and statistical modeling library that runs on JAX, providing tools for Bayesian inference, probabilistic layers, and advanced distributions with XLA-accelerated computation.
 E438358 NE FINISHED

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: TensorFlow Probability (JAX backend) | Statement: [JAX, compatibleWith, TensorFlow Probability (JAX backend)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TensorFlow Probability (JAX backend)
Context triple: [JAX, compatibleWith, TensorFlow Probability (JAX backend)]
  • A. TensorFlow Extended
    TensorFlow Extended (TFX) is an end-to-end platform for deploying, managing, and scaling production machine learning pipelines built on TensorFlow.
  • B. TensorFlow
    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.
  • C. 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.
  • D. Swift for TensorFlow
    Swift for TensorFlow is an experimental machine learning platform that integrates TensorFlow directly into the Swift programming language to enable differentiable programming and high-performance model development.
  • E. TPUs (via XLA integrations)
    TPUs (via XLA integrations) are Google's specialized tensor processing units that can be used as accelerators for PyTorch models through the XLA compilation framework.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: TensorFlow Probability (JAX backend)
Triple: [JAX, compatibleWith, TensorFlow Probability (JAX backend)]
Generated description
TensorFlow Probability (JAX backend) is a probabilistic programming and statistical modeling library that runs on JAX, providing tools for Bayesian inference, probabilistic layers, and advanced distributions with XLA-accelerated computation.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TensorFlow Probability (JAX backend)
Target entity description: TensorFlow Probability (JAX backend) is a probabilistic programming and statistical modeling library that runs on JAX, providing tools for Bayesian inference, probabilistic layers, and advanced distributions with XLA-accelerated computation.
  • A. TensorFlow Extended
    TensorFlow Extended (TFX) is an end-to-end platform for deploying, managing, and scaling production machine learning pipelines built on TensorFlow.
  • B. TensorFlow
    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.
  • C. 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.
  • D. Swift for TensorFlow
    Swift for TensorFlow is an experimental machine learning platform that integrates TensorFlow directly into the Swift programming language to enable differentiable programming and high-performance model development.
  • E. TPUs (via XLA integrations)
    TPUs (via XLA integrations) are Google's specialized tensor processing units that can be used as accelerators for PyTorch models through the XLA compilation framework.
  • F. None of above. chosen

Provenance (5 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_69b3453c2a0c8190926b574c90766db9 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3554e40ec8190982acc0948da2f42 completed March 13, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69b5f633a69c8190b062c2a78b0f8319 completed March 14, 2026, 11:58 p.m.
NEDg Description generation batch_69b5f6bcfa0481909d07ffb2a975a350 completed March 15, 2026, 12:01 a.m.
NED2 Entity disambiguation (via description) batch_69b5f733c660819081c68dc3ec342e12 completed March 15, 2026, 12:02 a.m.
Created at: March 12, 2026, 11:30 p.m.