Triple

T18016607
Position Surface form Disambiguated ID Type / Status
Subject KeypointRCNN E431011 entity
Predicate uses P98 FINISHED
Object RoIAlign — 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: RoIAlign | Statement: [KeypointRCNN, uses, RoIAlign]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: RoIAlign
Context triple: [KeypointRCNN, uses, RoIAlign]
  • A. R-CNN
    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. 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.
  • C. RCNN
    RCNN is the ICAO airport code assigned to Tainan Airport in Tainan, Taiwan.
  • D. DETR
    DETR is the acronym for the former UK government Department of the Environment, Transport and the Regions, which was responsible for environmental policy, transport, and regional affairs.
  • E. DETR
    DETR (Detection Transformer) is a deep learning model that applies transformer architectures to end-to-end object detection in images, eliminating the need for traditional hand-designed detection components.
  • 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: RoIAlign
Target entity description: RoIAlign is a computer vision operation that precisely extracts fixed-size feature maps from regions of interest in convolutional feature maps, commonly used in modern object detection and instance segmentation models.
  • A. R-CNN
    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. MaskRCNN chosen
    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.
  • C. RCNN
    RCNN is the ICAO airport code assigned to Tainan Airport in Tainan, Taiwan.
  • D. DETR
    DETR is the acronym for the former UK government Department of the Environment, Transport and the Regions, which was responsible for environmental policy, transport, and regional affairs.
  • E. DETR
    DETR (Detection Transformer) is a deep learning model that applies transformer architectures to end-to-end object detection in images, eliminating the need for traditional hand-designed detection components.
  • F. None of above.

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_69d8b904530081908bf341d842464856 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4b9be5d0c819097e006f32d98753a completed April 19, 2026, 11:17 a.m.
Created at: April 10, 2026, 10:24 a.m.