Triple
T15532299
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shaoqing Ren |
E370248
|
entity |
| Predicate | usedIn |
P98
|
FINISHED |
| Object |
COCO object detection benchmarks
COCO object detection benchmarks are widely used large-scale evaluation standards for measuring and comparing the performance of object detection algorithms on the COCO dataset.
|
E1162637
|
NE FINISHED |
How this triple was built (4 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: COCO object detection benchmarks | Statement: [Shaoqing Ren, usedIn, COCO object detection benchmarks]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: COCO object detection benchmarks Context triple: [Shaoqing Ren, usedIn, COCO object detection benchmarks]
-
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.
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.
-
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.
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.
-
E.
YOLO
"YOLO" is a comedic hip-hop song and music video by The Lonely Island, featuring Adam Levine and Kendrick Lamar, that parodies the phrase "you only live once" by humorously promoting extreme caution and risk avoidance.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: COCO object detection benchmarks Triple: [Shaoqing Ren, usedIn, COCO object detection benchmarks]
Generated description
COCO object detection benchmarks are widely used large-scale evaluation standards for measuring and comparing the performance of object detection algorithms on the COCO dataset.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: COCO object detection benchmarks Target entity description: COCO object detection benchmarks are widely used large-scale evaluation standards for measuring and comparing the performance of object detection algorithms on the COCO dataset.
-
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.
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.
-
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.
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.
-
E.
YOLO
"YOLO" is a comedic hip-hop song and music video by The Lonely Island, featuring Adam Levine and Kendrick Lamar, that parodies the phrase "you only live once" by humorously promoting extreme caution and risk avoidance.
- F. None of above. chosen
Provenance (5 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_69ff3d5e82a48190bb0a10ebc2412129 |
completed | May 9, 2026, 1:57 p.m. |
| NEDg | Description generation | batch_69ff3ea7d5ac81908bd1ee64de39dba7 |
completed | May 9, 2026, 2:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff413a68488190a6c8907e36a602dc |
completed | May 9, 2026, 2:14 p.m. |
Created at: April 10, 2026, 4:06 a.m.