GPT-3
E18819
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.
All labels observed (13)
| Label | Occurrences |
|---|---|
| GPT-3 canonical | 17 |
| GPT | 1 |
| GPT-3 1.3B | 1 |
| GPT-3 125M | 1 |
| GPT-3 13B | 1 |
| GPT-3 175B | 1 |
| GPT-3 2.7B | 1 |
| GPT-3 350M | 1 |
| GPT-3 6.7B | 1 |
| GPT-3 paper | 1 |
| Generative Pre-trained Transformer 3 | 1 |
| Language Models are Few-Shot Learners | 1 |
| OpenAI GPT-3 | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T146313 — 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: GPT-3 Context triple: [OpenAI, developed, GPT-3]
-
A.
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.
-
B.
GPT-3.5
GPT-3.5 is a large language model that generates human-like text and powers conversational AI applications such as advanced chatbots and coding assistants.
-
C.
DALL·E
DALL·E is an AI model developed by OpenAI that generates images from natural language descriptions, enabling text-to-image synthesis.
-
D.
OpenAI
OpenAI is an artificial intelligence research organization best known for developing advanced AI models such as ChatGPT and GPT series.
-
E.
Claude
Claude is a given name most famously associated with Claude Shannon, the American mathematician and electrical engineer known as the father of information theory.
- 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: GPT-3 Target entity description: 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.
-
A.
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.
-
B.
GPT-3.5
GPT-3.5 is a large language model that generates human-like text and powers conversational AI applications such as advanced chatbots and coding assistants.
-
C.
DALL·E
DALL·E is an AI model developed by OpenAI that generates images from natural language descriptions, enabling text-to-image synthesis.
-
D.
OpenAI
OpenAI is an artificial intelligence research organization best known for developing advanced AI models such as ChatGPT and GPT series.
-
E.
Claude
Claude is a given name most famously associated with Claude Shannon, the American mathematician and electrical engineer known as the father of information theory.
- F. None of above. chosen
Statements (57)
| Predicate | Object |
|---|---|
| instanceOf |
autoregressive language model
ⓘ
large language model ⓘ transformer-based model ⓘ |
| abbreviation | GPT-3 ⓘ |
| announcedIn | 2020 ⓘ |
| architecture | Transformer ⓘ |
| capability |
few-shot learning
ⓘ
natural language generation ⓘ one-shot learning ⓘ question answering ⓘ summarization ⓘ text completion ⓘ translation ⓘ zero-shot learning ⓘ |
| commercialAccess |
OpenAI API
ⓘ
linked to:
OpenAI Chat Completions API
|
| describedInPaper |
Language Models are Few-Shot Learners
ⓘ
linked to:
GPT-3
|
| developer | OpenAI ⓘ |
| fewShotPrompting | supported ⓘ |
| fineTuning | supports task-specific fine-tuning ⓘ |
| fullName |
Generative Pre-trained Transformer 3
ⓘ
linked to:
GPT-3
|
| inputType | text ⓘ |
| language | English ⓘ |
| license | proprietary ⓘ |
| limitation |
can generate incorrect or fabricated information
ⓘ
may reflect biases in training data ⓘ sensitive to prompt phrasing ⓘ |
| modelSize | 175 billion parameters ⓘ |
| notableVariant |
GPT-3 1.3B
ⓘ
linked to:
GPT-3
GPT-3 125M ⓘ
linked to:
GPT-3
GPT-3 13B ⓘ
linked to:
GPT-3
GPT-3 175B ⓘ
linked to:
GPT-3
GPT-3 2.7B ⓘ
linked to:
GPT-3
GPT-3 350M ⓘ
linked to:
GPT-3
GPT-3 6.7B ⓘ
linked to:
GPT-3
|
| numberOfParametersOfLargestVariant | 175B ⓘ |
| outputType | text ⓘ |
| paperAuthors | Tom B. Brown et al. ⓘ |
| parameterCount | 175,000,000,000 ⓘ |
| predecessor | GPT-2 ⓘ |
| releaseDate | June 2020 ⓘ |
| safetyMitigations | content filters via OpenAI API ⓘ |
| successor |
GPT-3.5
ⓘ
GPT-4 ⓘ |
| trainingCompute | hundreds of petaflop/s-days (approximate) ⓘ |
| trainingDataCutoff | October 2019 ⓘ |
| trainingDataSource |
Common Crawl
ⓘ
WebText-like corpora ⓘ Wikipedia ⓘ books ⓘ |
| trainingObjective | next-token prediction ⓘ |
| trainingParadigm |
self-supervised learning
ⓘ
unsupervised pre-training ⓘ |
| useCase |
chatbots
ⓘ
code generation (via Codex derivatives) ⓘ content creation ⓘ virtual assistants ⓘ |
| zeroShotPrompting | supported ⓘ |
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: GPT-3 Description of subject: 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.
Referenced by (29)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: GPT-3
linked to: GPT-3
linked to: GPT-3
linked to: GPT-3
linked to: GPT-3
linked to: GPT-3
linked to: GPT-3
linked to: GPT-3
linked to: GPT-3
subject linked to:
Tom B. Brown et al.
subject linked to:
Tom B. Brown et al.
linked to: GPT-3
linked to: GPT-3