Blocks
E435218
Blocks is a Python deep learning framework built on top of Theano that provides modular, reusable components for constructing and training neural networks.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Blocks canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4390985 — 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: Blocks Context triple: [Theano, usedAsBackendFor, Blocks]
-
A.
Block Z
Block Z was the specific spectator section in Brussels’ Heysel Stadium where the fatal crowd crush occurred during the 1985 European Cup final, making it central to the Heysel Stadium disaster.
-
B.
H-blocks
H-blocks were the distinctive H-shaped cell blocks within HM Prison Maze in Northern Ireland, notorious for housing paramilitary prisoners during the Troubles and for being the site of protests and hunger strikes.
-
C.
Steps
Steps is a British pop group known for their catchy dance-pop hits and choreographed performances, particularly popular in the late 1990s and early 2000s.
-
D.
Block O
Block O is the famously large and energetic student cheering section for Ohio State Buckeyes football games at Ohio Stadium.
-
E.
Blok DM
Blok DM is a Russian upper stage used primarily on Proton and Zenit launch vehicles to deliver payloads into high-energy orbits such as geostationary transfer and lunar trajectories.
- 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: Blocks Target entity description: Blocks is a Python deep learning framework built on top of Theano that provides modular, reusable components for constructing and training neural networks.
-
A.
Block Z
Block Z was the specific spectator section in Brussels’ Heysel Stadium where the fatal crowd crush occurred during the 1985 European Cup final, making it central to the Heysel Stadium disaster.
-
B.
H-blocks
H-blocks were the distinctive H-shaped cell blocks within HM Prison Maze in Northern Ireland, notorious for housing paramilitary prisoners during the Troubles and for being the site of protests and hunger strikes.
-
C.
Steps
Steps is a British pop group known for their catchy dance-pop hits and choreographed performances, particularly popular in the late 1990s and early 2000s.
-
D.
Block O
Block O is the famously large and energetic student cheering section for Ohio State Buckeyes football games at Ohio Stadium.
-
E.
Blok DM
Blok DM is a Russian upper stage used primarily on Proton and Zenit launch vehicles to deliver payloads into high-energy orbits such as geostationary transfer and lunar trajectories.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
deep learning framework
ⓘ
open-source software ⓘ |
| basedOn | Theano ⓘ |
| category |
Python library
ⓘ
machine learning library ⓘ |
| designGoal |
composability of neural network components
ⓘ
separation of model definition and training algorithm ⓘ |
| developedAt | Université de Montréal ⓘ |
| developedBy |
MILA
ⓘ
linked to:
Mila
|
| feature |
bricks abstraction
ⓘ
checkpointing ⓘ graph annotations ⓘ logging ⓘ monitoring extensions ⓘ regularization utilities ⓘ training algorithms ⓘ |
| hostedOn | GitHub ⓘ |
| integratesWith | Fuel ⓘ |
| license | MIT License ⓘ |
| primaryUse |
prototyping neural network models
ⓘ
research in deep learning ⓘ |
| programmingLanguage | Python ⓘ |
| provides |
modular components
ⓘ
reusable components ⓘ |
| relatedTo |
Fuel data pipeline library
ⓘ
Theano numerical computation library ⓘ
linked to:
Theano
|
| repositoryName | mila-udem/blocks ⓘ |
| status | no longer actively maintained ⓘ |
| supports |
AdaDelta
ⓘ
AdaGrad ⓘ Adam optimizer ⓘ GPU acceleration via Theano ⓘ GRU networks ⓘ LSTM networks ⓘ RMSProp ⓘ batch normalization ⓘ convolutional neural networks ⓘ dropout regularization ⓘ early stopping ⓘ feedforward neural networks ⓘ gradient descent optimization ⓘ learning rate scheduling ⓘ mini-batch training ⓘ neural network construction ⓘ neural network training ⓘ recurrent neural networks ⓘ weight decay ⓘ |
| uses | Theano computation graphs ⓘ |
| writtenIn | Python ⓘ |
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: Blocks Description of subject: Blocks is a Python deep learning framework built on top of Theano that provides modular, reusable components for constructing and training neural networks.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.