Triple

T18016495
Position Surface form Disambiguated ID Type / Status
Subject Mask R-CNN E431009 entity
Predicate instanceOf P0 FINISHED
Object object detection model C31656 CONCEPT FINISHED

How this triple was built (1 step)

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.

CD Concept disambiguation gpt-5-mini-2025-08-07 
Target class: object detection model
Context triple: [Mask R-CNN, instanceOf, object detection model]
  • A. image recognition model
    An image recognition model is a computational system that analyzes visual input to automatically identify, classify, and sometimes localize objects, patterns, or features within images.
  • B. deep learning model
    A deep learning model is a computational architecture composed of multiple layers of interconnected processing units (neurons) that automatically learn hierarchical representations from data to perform tasks such as classification, prediction, or generation.
  • C. computer vision algorithm chosen
    A computer vision algorithm is a computational method that processes and interprets visual data from images or videos to automatically extract meaningful information or perform tasks such as detection, recognition, and segmentation.
  • D. image captioning model
    An image captioning model is a system that automatically generates descriptive natural language sentences that explain the content of an input image.
  • E. image generation model
    An image generation model is an AI system that creates new images from input data such as text prompts, reference images, or learned patterns, using techniques like deep neural networks and generative modeling.
  • F. None of above.

Provenance (1 batch)

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.
Created at: April 10, 2026, 10:24 a.m.