LLaMA
E435872
LLaMA is a family of large language models developed by Meta AI, designed for efficient training and inference across a range of natural language processing tasks.
All labels observed (2)
How this entity was disambiguated
This entity first appeared as the object of triple T4389199 — 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: LLaMA Context triple: [Hugging Face Transformers, supportsModelType, LLaMA]
-
A.
LLM
LLM is the ICAO airline designator assigned to Yamal Airlines, a Russian regional carrier.
-
B.
GPT-3
GPT-3 is a large-scale autoregressive language model known for generating human-like text and performing a wide range of natural language tasks with minimal fine-tuning.
-
C.
GPT-2
GPT-2 is a large transformer-based language model known for generating coherent, human-like text and sparking widespread discussion about the implications of advanced AI text generation.
-
D.
Hugging Face Transformers
Hugging Face Transformers is a widely used open-source library that provides state-of-the-art transformer-based models and tools for natural language processing and related machine learning tasks.
-
E.
DALL·E
DALL·E is an AI model developed by OpenAI that generates images from natural language descriptions, enabling text-to-image synthesis.
- 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: LLaMA Target entity description: LLaMA is a family of large language models developed by Meta AI, designed for efficient training and inference across a range of natural language processing tasks.
-
A.
LLM
LLM is the ICAO airline designator assigned to Yamal Airlines, a Russian regional carrier.
-
B.
GPT-3
GPT-3 is a large-scale autoregressive language model known for generating human-like text and performing a wide range of natural language tasks with minimal fine-tuning.
-
C.
GPT-2
GPT-2 is a large transformer-based language model known for generating coherent, human-like text and sparking widespread discussion about the implications of advanced AI text generation.
-
D.
Hugging Face Transformers
Hugging Face Transformers is a widely used open-source library that provides state-of-the-art transformer-based models and tools for natural language processing and related machine learning tasks.
-
E.
DALL·E
DALL·E is an AI model developed by OpenAI that generates images from natural language descriptions, enabling text-to-image synthesis.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf | large language model family ⓘ |
| abbreviationOf | Large Language Model Meta AI NERFINISHED ⓘ |
| architectureType | decoder-only transformer ⓘ |
| basedOn | transformer architecture ⓘ |
| computingParadigm | deep learning ⓘ |
| developer |
Meta AI
NERFINISHED
ⓘ
Meta Platforms NERFINISHED ⓘ |
| domain | artificial intelligence ⓘ |
| field | natural language processing ⓘ |
| hasSuccessor |
LLaMA 2
NERFINISHED
ⓘ
LLaMA 3 NERFINISHED ⓘ |
| inspired |
LLaMA 2
NERFINISHED
ⓘ
LLaMA 3 NERFINISHED ⓘ |
| language |
English
ⓘ
multilingual text ⓘ |
| license | research license (original release) ⓘ |
| notableFor |
open research access compared to many contemporaries
ⓘ
strong performance at smaller parameter scales ⓘ |
| optimizationGoal |
efficient inference
ⓘ
efficient training ⓘ |
| organization | Meta AI FAIR (Fundamental AI Research) NERFINISHED ⓘ |
| parameterCountVariant |
13B
ⓘ
33B ⓘ 65B ⓘ 7B ⓘ |
| relatedTo |
GPT family
NERFINISHED
ⓘ
Mistral NERFINISHED ⓘ PaLM NERFINISHED ⓘ |
| releaseDate | 2023-02 ⓘ |
| supports |
few-shot learning
ⓘ
fine-tuning ⓘ instruction tuning ⓘ prompt-based conditioning ⓘ zero-shot learning ⓘ |
| task |
code generation
ⓘ
question answering ⓘ summarization ⓘ text completion ⓘ text generation ⓘ translation ⓘ |
| trainingDataType |
Wikipedia
NERFINISHED
ⓘ
books ⓘ code ⓘ public web data ⓘ |
| trainingObjective | causal language modeling ⓘ |
| uses |
downstream NLP applications
ⓘ
research in large language models ⓘ |
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: LLaMA Description of subject: LLaMA is a family of large language models developed by Meta AI, designed for efficient training and inference across a range of natural language processing tasks.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Llama 3