Flax
E438356
Flax is a neural network library for JAX that provides a flexible, modular framework for building and training machine learning models in Python.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Flax canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4425382 — 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: Flax Context triple: [JAX, compatibleWith, Flax]
-
A.
Hahnenklee
Hahnenklee is a village in the Harz Mountains of Germany, known as a popular tourist resort for hiking, winter sports, and its distinctive stave church.
-
B.
Avena fatua
Avena fatua, commonly known as wild oat, is a widespread annual grass species often considered a weed in agricultural and disturbed habitats.
-
C.
Fagopyrum
Fagopyrum is a genus of flowering plants best known for species such as buckwheat, which are cultivated for their edible seeds and use as pseudocereals.
-
D.
Dill
The Dill is a river in central Germany that flows through Hesse and North Rhine-Westphalia before joining the Lahn.
-
E.
Lucerne
Lucerne is a picturesque Swiss city known for its preserved medieval architecture, lakeside setting on Lake Lucerne, and proximity to the Swiss Alps.
- 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: Flax Target entity description: Flax is a neural network library for JAX that provides a flexible, modular framework for building and training machine learning models in Python.
-
A.
Hahnenklee
Hahnenklee is a village in the Harz Mountains of Germany, known as a popular tourist resort for hiking, winter sports, and its distinctive stave church.
-
B.
Avena fatua
Avena fatua, commonly known as wild oat, is a widespread annual grass species often considered a weed in agricultural and disturbed habitats.
-
C.
Fagopyrum
Fagopyrum is a genus of flowering plants best known for species such as buckwheat, which are cultivated for their edible seeds and use as pseudocereals.
-
D.
Dill
The Dill is a river in central Germany that flows through Hesse and North Rhine-Westphalia before joining the Lahn.
-
E.
Lucerne
Lucerne is a picturesque Swiss city known for its preserved medieval architecture, lakeside setting on Lake Lucerne, and proximity to the Swiss Alps.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
neural network library
ⓘ
open-source software ⓘ software library ⓘ |
| basedOn | JAX ⓘ |
| compatibleWith |
NumPy API via JAX
ⓘ
linked to:
jax.experimental
Optax ⓘ Orbax ⓘ |
| designGoal |
composability
ⓘ
explicit handling of state and randomness ⓘ flexibility ⓘ |
| developedBy |
Google
ⓘ
Google Research ⓘ |
| domain |
deep learning
ⓘ
machine learning ⓘ |
| ecosystem | JAX ecosystem ⓘ |
| hasComponent |
flax.linen
ⓘ
flax.optim (deprecated in favor of Optax) ⓘ flax.training ⓘ |
| hasFeature |
checkpointing utilities
ⓘ
module abstraction (flax.linen) ⓘ parameter management ⓘ random number handling ⓘ serialization utilities ⓘ state management ⓘ training loops utilities ⓘ |
| hostedOn | GitHub ⓘ |
| license | Apache License 2.0 ⓘ |
| programmingLanguage | Python ⓘ |
| provides |
flexible neural network framework
ⓘ
modular framework for building models ⓘ tools for training machine learning models ⓘ |
| repositoryURL | https://github.com/google/flax ⓘ |
| supports |
JAX transformations
ⓘ
NLP models ⓘ convolutional neural networks ⓘ functional programming style ⓘ gradient-based optimization ⓘ model training ⓘ neural network construction ⓘ recurrent neural networks ⓘ reinforcement learning models ⓘ stateful neural network modules ⓘ transformer models ⓘ vision models ⓘ |
| supportsLanguage | Python ⓘ |
| usedFor |
production ML systems
ⓘ
rapid prototyping of models ⓘ research in machine learning ⓘ |
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: Flax Description of subject: Flax is a neural network library for JAX that provides a flexible, modular framework for building and training machine learning models in Python.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.