Triple

T18016572
Position Surface form Disambiguated ID Type / Status
Subject RetinaNet E431010 entity
Predicate implementationFramework P9089 FINISHED
Object Caffe2 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: Caffe2 | Statement: [RetinaNet, implementationFramework, Caffe2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Caffe2
Context triple: [RetinaNet, implementationFramework, Caffe2]
  • A. Caffe2 chosen
    Caffe2 is a lightweight, modular deep learning framework developed by Facebook (Meta) designed for scalable training and deployment of neural networks on mobile and large-scale production environments.
  • B. PaddlePaddle
    PaddlePaddle is an open-source deep learning platform developed by Baidu, designed for large-scale distributed training and deployment of neural networks.
  • C. NVIDIA TensorRT
    NVIDIA TensorRT is a high-performance deep learning inference optimizer and runtime library designed to accelerate AI models on NVIDIA GPUs in production environments.
  • D. PyTorch
    PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
  • E. TensorFlow Metal
    TensorFlow Metal is an integration that enables TensorFlow to run efficiently on Apple GPUs via the Metal framework, accelerating machine learning workloads on macOS and iOS devices.
  • 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: implementationFramework
Context triple: [RetinaNet, implementationFramework, Caffe2]
  • A. frameworkFor
    Indicates that one entity serves as a supporting structure, system, or basis that organizes, guides, or enables the development or functioning of another entity.
  • B. appliesFramework chosen
    Indicates that one entity uses or implements a particular framework in relation to another entity or context.
  • C. adoptedFrameworkType
    Indicates that one entity has chosen and is using a particular framework type as its adopted standard or approach.
  • D. frameworkName
    Indicates that a specific framework is identified by the given name.
  • E. frameworkLayer
    Indicates a relationship where one framework is organized within, built upon, or conceptually assigned to a particular architectural or conceptual layer.
  • F. None of above.

Provenance (3 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_69d8b904530081908bf341d842464856 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4b9be5d0c819097e006f32d98753a completed April 19, 2026, 11:17 a.m.
PD Predicate disambiguation batch_69e3f904b8048190add43883cd7cb191 completed April 18, 2026, 9:35 p.m.
Created at: April 10, 2026, 10:24 a.m.