Triple

T18016472
Position Surface form Disambiguated ID Type / Status
Subject Faster R-CNN E431008 entity
Predicate improvesOver P6555 FINISHED
Object Fast R-CNN — NE NERFINISHED

Named-entity recognition

Before disambiguation, gpt-5-mini classified whether the object phrase is a named entity — the step behind the object's NE type shown above.

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: Fast R-CNN | Statement: [Faster R-CNN, improvesOver, Fast R-CNN]

Disambiguation candidates (1 decision)

The exact options the model was shown at each disambiguation step, with the option it chose highlighted — the evidence behind this triple's disambiguated ids.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Fast R-CNN
Context triple: [Faster R-CNN, improvesOver, Fast R-CNN]
  • A. R-CNN chosen
    R-CNN is a pioneering deep learning framework for object detection that combines region proposals with convolutional neural networks to accurately localize and classify objects in images.
  • B. RCNN
    RCNN is the ICAO airport code assigned to Tainan Airport in Tainan, Taiwan.
  • 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. Region Proposal Network
    A Region Proposal Network is a deep learning module that efficiently generates candidate object bounding boxes directly from feature maps for use in modern object detection systems.
  • E. Feature Pyramid Networks based detectors
    Feature Pyramid Networks based detectors are a family of object detection models that enhance multi-scale feature representation by building top-down feature hierarchies with lateral connections, improving accuracy for objects of varying sizes.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 batches)

Stage Batch ID Job type Status
creating batch_69d8b904530081908bf341d842464856 elicitation completed
NER batch_69e4b523f588819097389e067dda7f23 ner completed
Created at: April 10, 2026, 10:24 a.m.