GPT-1
E469810
GPT-1 is the first-generation Generative Pre-trained Transformer language model developed by OpenAI, introducing the pretrain-then-finetune paradigm for large-scale NLP.
All labels observed (1)
| Label | Occurrences |
|---|---|
| GPT-1 canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T4804625 — 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-1 Context triple: [GPT series, hasMember, GPT-1]
-
A.
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.
-
B.
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.
-
C.
GPT-Neo
GPT-Neo is an open-source family of autoregressive language models developed by EleutherAI as a free alternative to OpenAI’s GPT-3.
-
D.
GPT
GPT is a family of large language models developed by OpenAI that can understand and generate human-like text for a wide range of tasks.
-
E.
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.
- 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-1 Target entity description: GPT-1 is the first-generation Generative Pre-trained Transformer language model developed by OpenAI, introducing the pretrain-then-finetune paradigm for large-scale NLP.
-
A.
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.
-
B.
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.
-
C.
GPT-Neo
GPT-Neo is an open-source family of autoregressive language models developed by EleutherAI as a free alternative to OpenAI’s GPT-3.
-
D.
GPT
GPT is a family of large language models developed by OpenAI that can understand and generate human-like text for a wide range of tasks.
-
E.
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.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
Generative Pre-trained Transformer
ⓘ
autoregressive language model ⓘ large language model ⓘ |
| architectureDepth | 12 layers ⓘ |
| basedOn | Transformer architecture ⓘ |
| coAuthor |
Ilya Sutskever
ⓘ
Karthik Narasimhan ⓘ
linked to:
Arvind Neelakantan
Tim Salimans ⓘ |
| designGoal |
improve sample efficiency for NLP tasks
ⓘ
leverage unsupervised data for representation learning ⓘ |
| developer | OpenAI ⓘ |
| field |
artificial intelligence
ⓘ
machine learning ⓘ natural language processing ⓘ |
| fineTuningTasks |
natural language inference
ⓘ
question answering ⓘ reading comprehension ⓘ semantic similarity ⓘ text classification ⓘ |
| improvedOver | task-specific models trained from scratch ⓘ |
| inferenceMode | left-to-right generation ⓘ |
| influenced |
GPT-2
ⓘ
GPT-3 ⓘ subsequent large language models ⓘ |
| inputType | text ⓘ |
| introducedConcept |
large-scale unsupervised pretraining for NLP
ⓘ
task-specific supervised fine-tuning after pretraining ⓘ |
| language | English ⓘ |
| modelType | unidirectional transformer ⓘ |
| notableContribution |
demonstrated transfer learning in NLP
ⓘ
showed that a single pretrained model can be adapted to many tasks ⓘ |
| numberOfParameters | 117M ⓘ |
| organization | OpenAI ⓘ |
| outputType | text ⓘ |
| parameterCountCategory | hundreds of millions of parameters ⓘ |
| pretrainingDataType | BooksCorpus-like web text ⓘ |
| pretrainingTask | language modeling ⓘ |
| primaryAuthor | Alec Radford ⓘ |
| publicationTitle | Improving Language Understanding by Generative Pre-Training ⓘ |
| publicationVenue |
OpenAI technical report
ⓘ
linked to:
OpenAI
|
| publicationYear | 2018 ⓘ |
| tokenizerType | Byte Pair Encoding ⓘ |
| trainingMethod |
supervised fine-tuning
ⓘ
unsupervised pretraining ⓘ |
| trainingObjective | next-token prediction ⓘ |
| trainingParadigm | pretrain-then-finetune ⓘ |
| uses |
multi-head self-attention
ⓘ
positional embeddings ⓘ |
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-1 Description of subject: GPT-1 is the first-generation Generative Pre-trained Transformer language model developed by OpenAI, introducing the pretrain-then-finetune paradigm for large-scale NLP.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.