Triple

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).

Phrase: ResNet-101-FPN | Statement: [KeypointRCNN, typicalBackbone, ResNet-101-FPN]

Target entity: ResNet-101-FPN
Context triple: [KeypointRCNN, typicalBackbone, ResNet-101-FPN]

• A. RetinaNet
  RetinaNet is a deep learning–based one-stage object detection model known for its focal loss function, which effectively addresses class imbalance to achieve high accuracy and speed.
• B. ResNet
  ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
• C. FasterRCNN
  FasterRCNN is a popular two-stage object detection architecture that first proposes candidate regions and then classifies and refines bounding boxes, widely used in computer vision tasks.
• D. FPN chosen
  FPN (Feature Pyramid Network) is a neural network architecture component that builds multi-scale feature maps to improve object detection performance, especially for detecting objects at different sizes.
• E. MaskRCNN
  MaskRCNN is a deep learning model architecture for instance segmentation that extends Faster R-CNN by adding a branch to predict segmentation masks for individual objects in an image.
• F. None of above.
• G. Unsure - the case is ambiguous/there is not enough information to decide.