Triple

T15361330
Position Surface form Disambiguated ID Type / Status
Subject Kaiming He E367295 entity
Predicate knownFor P22 FINISHED
Object Mask R-CNN E431009 NE FINISHED

How this triple was built (2 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: Mask R-CNN | Statement: [Kaiming He, knownFor, Mask R-CNN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mask R-CNN
Context triple: [Kaiming He, knownFor, Mask R-CNN]
  • A. 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.
  • 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. 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.
  • E. KeypointRCNN
    KeypointRCNN is a deep learning model architecture in PyTorch’s torchvision library designed for object detection combined with human pose estimation via keypoint prediction.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d85a1483788190ad93c2748e8af34b completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e4607408190ab281a7f7a8012d3 completed April 16, 2026, 1:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff0b4a181c8190bffc1ac1a86e215d completed May 9, 2026, 10:24 a.m.
Created at: April 10, 2026, 3:18 a.m.