Triple

T9675007
Position Surface form Disambiguated ID Type / Status
Subject NVIDIA Triton Inference Server E234124 entity
Predicate supportsFormat P203 FINISHED
Object 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.
 E813079 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 GraphDef | Statement: [NVIDIA Triton Inference Server, supportsFormat, TensorFlow GraphDef]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TensorFlow GraphDef
Context triple: [NVIDIA Triton Inference Server, supportsFormat, TensorFlow GraphDef]
  • A. TensorFlow Serving
    TensorFlow Serving is a flexible, high-performance system for deploying and serving machine learning models in production, particularly those built with TensorFlow.
  • B. TensorFlow Extended
    TensorFlow Extended (TFX) is an end-to-end platform for deploying, managing, and scaling production machine learning pipelines built on TensorFlow.
  • C. 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.
  • 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.
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 GraphDef
Triple: [NVIDIA Triton Inference Server, supportsFormat, TensorFlow GraphDef]
Generated description
TensorFlow GraphDef is a serialized protocol buffer format that represents the computational graph structure of a TensorFlow model, including its operations and data flow.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TensorFlow GraphDef
Target entity description: TensorFlow GraphDef is a serialized protocol buffer format that represents the computational graph structure of a TensorFlow model, including its operations and data flow.
  • A. TensorFlow Serving
    TensorFlow Serving is a flexible, high-performance system for deploying and serving machine learning models in production, particularly those built with TensorFlow.
  • B. TensorFlow Extended
    TensorFlow Extended (TFX) is an end-to-end platform for deploying, managing, and scaling production machine learning pipelines built on TensorFlow.
  • C. 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.
  • 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

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_69ca848f55e48190b3f67252571c3d45 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9c6d6dd48190a77c486337a58cb6 completed April 1, 2026, 10:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69d18a30883081909e8f70225b6ee820 completed April 4, 2026, 10:01 p.m.
NEDg Description generation batch_69d18ac0796c8190b48ccdb9c5052332 completed April 4, 2026, 10:03 p.m.
NED2 Entity disambiguation (via description) batch_69d18b7f7510819083a402d6802c7d95 completed April 4, 2026, 10:06 p.m.
Created at: March 30, 2026, 8:15 p.m.