Triple

T17598484
Position Surface form Disambiguated ID Type / Status
Subject TensorFlow ecosystem E428633 entity
Predicate includesComponent P1393 FINISHED
Object TensorFlow Cloud — 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 Cloud | Statement: [TensorFlow ecosystem, includesComponent, TensorFlow Cloud]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TensorFlow Cloud
Context triple: [TensorFlow ecosystem, includesComponent, TensorFlow Cloud]
  • 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. Google Cloud TPU V2
    Google Cloud TPU V2 is a second-generation tensor processing unit offered as a cloud service by Google, designed to accelerate large-scale machine learning workloads such as deep neural network training and inference.
  • C. TensorFlow Hub
    TensorFlow Hub is a library and online repository of reusable machine learning models and components designed to simplify sharing and deploying pretrained models in TensorFlow applications.
  • D. Google TPU
    Google TPU is a custom-designed application-specific integrated circuit (ASIC) developed by Google to accelerate machine learning workloads, particularly deep learning inference and training in its data centers.
  • E. Google Cloud TPU V3
    Google Cloud TPU v3 is a high-performance, third-generation tensor processing unit offered on Google Cloud for accelerating large-scale machine learning and deep learning workloads.
  • 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 Cloud
Target entity description: TensorFlow Cloud is a library that simplifies running and scaling TensorFlow training workloads on Google Cloud directly from local or notebook-based development environments.
  • 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. Google Cloud TPU V2
    Google Cloud TPU V2 is a second-generation tensor processing unit offered as a cloud service by Google, designed to accelerate large-scale machine learning workloads such as deep neural network training and inference.
  • C. TensorFlow Hub
    TensorFlow Hub is a library and online repository of reusable machine learning models and components designed to simplify sharing and deploying pretrained models in TensorFlow applications.
  • D. Google TPU
    Google TPU is a custom-designed application-specific integrated circuit (ASIC) developed by Google to accelerate machine learning workloads, particularly deep learning inference and training in its data centers.
  • E. Google Cloud TPU V3
    Google Cloud TPU v3 is a high-performance, third-generation tensor processing unit offered on Google Cloud for accelerating large-scale machine learning and deep learning workloads.
  • F. None of above. chosen

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_69d889e1030481909950e140c63255b9 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e46c474e5481909d2736241b592dab completed April 19, 2026, 5:46 a.m.
Created at: April 10, 2026, 5:51 a.m.