Triple

T17521003
Position Surface form Disambiguated ID Type / Status
Subject TensorFlow SavedModel (via conversion) E426677 entity
Predicate inputFormatOf P80182 FINISHED
Object TensorFlow.js Graph 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 Graph model format | Statement: [TensorFlow SavedModel (via conversion), inputFormatOf, TensorFlow.js Graph model format]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TensorFlow.js Graph model format
Context triple: [TensorFlow SavedModel (via conversion), inputFormatOf, TensorFlow.js Graph model format]
  • A. 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.
  • B. TensorFlow GraphDef
    TensorFlow GraphDef is a serialized protocol buffer format that represents the computational graph structure of a TensorFlow model, including its operations and data flow.
  • 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 SavedModel (via conversion)
    TensorFlow SavedModel (via conversion) is a serialized model format from the core TensorFlow ecosystem that can be transformed into a TensorFlow.js-compatible model for deployment in JavaScript environments.
  • E. 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.
  • 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: TensorFlow.js Graph model format
Target entity description: TensorFlow.js Graph model format is a JavaScript-friendly representation of TensorFlow computation graphs that enables running pre-trained models directly in the browser or Node.js using WebGL or other backends.
  • A. 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.
  • B. TensorFlow GraphDef
    TensorFlow GraphDef is a serialized protocol buffer format that represents the computational graph structure of a TensorFlow model, including its operations and data flow.
  • 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 SavedModel (via conversion) chosen
    TensorFlow SavedModel (via conversion) is a serialized model format from the core TensorFlow ecosystem that can be transformed into a TensorFlow.js-compatible model for deployment in JavaScript environments.
  • E. 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.
  • F. None of above.

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_69d889de677081909b22d2657b1f0292 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e452d23cf08190925510344fa36f57 completed April 19, 2026, 3:58 a.m.
Created at: April 10, 2026, 5:49 a.m.