Triple
T15532271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shaoqing Ren |
E370248
|
entity |
| Predicate | coAuthorOf |
P2389
|
FINISHED |
| Object | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks |
E431008
|
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: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | Statement: [Shaoqing Ren, coAuthorOf, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Context triple: [Shaoqing Ren, coAuthorOf, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks]
-
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.
RCNN
RCNN is the ICAO airport code assigned to Tainan Airport in Tainan, Taiwan.
-
C.
FasterRCNN
chosen
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.
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.
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_69d85cc521a08190921fb50319dddc34 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e0414877d88190804ee76566004e13 |
completed | April 16, 2026, 1:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff45549e4881908ab0769e49ba5dc4 |
completed | May 9, 2026, 2:31 p.m. |
Created at: April 10, 2026, 4:06 a.m.