MSCOCO
E899057
MSCOCO is a large-scale benchmark dataset of everyday images with rich object annotations and human-written captions, widely used for training and evaluating computer vision and image captioning models.
All labels observed (1)
| Label | Occurrences |
|---|---|
| MSCOCO canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11003510 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MSCOCO Context triple: [Show and Tell: A Neural Image Caption Generator, usesDataset, MSCOCO]
-
A.
MCoE
MCoE is the U.S. Army’s primary training and doctrine center for maneuver forces, integrating infantry, armor, and related capabilities at Fort Moore, Georgia.
-
B.
MCPOCG
MCPOCG is the highest senior enlisted rank and principal enlisted advisor to the Commandant in the United States Coast Guard.
-
C.
MoC
MoC is the Saudi Ministry of Culture, the government body responsible for developing, preserving, and promoting Saudi Arabia’s cultural sector and heritage.
-
D.
MCS
MCS is the Mellon College of Science, a core academic division of Carnegie Mellon University known for its programs in the natural and mathematical sciences.
-
E.
MCS
MCS is the station code for the MediaCityUK tram stop on Greater Manchester’s Metrolink light rail network.
- 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: MSCOCO Target entity description: MSCOCO is a large-scale benchmark dataset of everyday images with rich object annotations and human-written captions, widely used for training and evaluating computer vision and image captioning models.
-
A.
MCoE
MCoE is the U.S. Army’s primary training and doctrine center for maneuver forces, integrating infantry, armor, and related capabilities at Fort Moore, Georgia.
-
B.
MCPOCG
MCPOCG is the highest senior enlisted rank and principal enlisted advisor to the Commandant in the United States Coast Guard.
-
C.
MoC
MoC is the Saudi Ministry of Culture, the government body responsible for developing, preserving, and promoting Saudi Arabia’s cultural sector and heritage.
-
D.
MCS
MCS is the Mellon College of Science, a core academic division of Carnegie Mellon University known for its programs in the natural and mathematical sciences.
-
E.
MCS
MCS is the station code for the MediaCityUK tram stop on Greater Manchester’s Metrolink light rail network.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
benchmark dataset
ⓘ
computer vision dataset ⓘ image captioning dataset ⓘ image dataset ⓘ |
| abbreviation | COCO NERFINISHED ⓘ |
| developedBy |
Microsoft
ⓘ
Microsoft Research NERFINISHED ⓘ |
| domain | computer vision ⓘ |
| evaluationMetric |
BLEU
NERFINISHED
ⓘ
CIDEr NERFINISHED ⓘ METEOR NERFINISHED ⓘ ROUGE-L NERFINISHED ⓘ SPICE NERFINISHED ⓘ mAP@[.5:.95] ⓘ mean Average Precision ⓘ |
| fullName | Microsoft Common Objects in Context NERFINISHED ⓘ |
| hasAnnotationType |
human-written captions
ⓘ
image-level labels ⓘ keypoints ⓘ object bounding boxes ⓘ object categories ⓘ object segmentation masks ⓘ |
| hasModality | natural images ⓘ |
| hasProperty |
context-rich images
ⓘ
everyday scenes ⓘ large-scale ⓘ multiple objects per image ⓘ rich annotations ⓘ |
| hasSplit |
test set
ⓘ
training set ⓘ validation set ⓘ |
| hasTask |
COCO Captioning Challenge
NERFINISHED
ⓘ
COCO Detection Challenge NERFINISHED ⓘ COCO Keypoints Challenge NERFINISHED ⓘ COCO Segmentation Challenge NERFINISHED ⓘ |
| isWidelyUsedIn |
academic research
ⓘ
benchmarking vision models ⓘ |
| license | non-commercial research license ⓘ |
| releasedBy | Microsoft COCO Consortium NERFINISHED ⓘ |
| usedFor |
deep learning research
ⓘ
human pose estimation ⓘ image captioning ⓘ instance segmentation ⓘ keypoint detection ⓘ model evaluation ⓘ object detection ⓘ object recognition ⓘ semantic segmentation ⓘ supervised learning ⓘ visual question answering ⓘ |
| website | https://cocodataset.org ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: MSCOCO Description of subject: MSCOCO is a large-scale benchmark dataset of everyday images with rich object annotations and human-written captions, widely used for training and evaluating computer vision and image captioning models.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.