Triple

T4804625
Position Surface form Disambiguated ID Type / Status
Subject GPT series E106918 entity
Predicate hasMember P10 FINISHED
Object GPT-1
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.
 E469810 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: GPT-1 | Statement: [GPT series, hasMember, GPT-1]
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.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: GPT-1
Triple: [GPT series, hasMember, GPT-1]
Generated 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.
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

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69bd43f6a1e08190bf0a372bfc336ee5 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6c664c3c81908e4d9a7c8c19744b completed March 20, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69be4403ef888190b41c9a0db6bf47aa completed March 21, 2026, 7:08 a.m.
NEDg Description generation batch_69be44df4e808190bb65ea205446a98f completed March 21, 2026, 7:12 a.m.
NED2 Entity disambiguation (via description) batch_69be45bc7b2c8190aa293d2c10077864 completed March 21, 2026, 7:16 a.m.
Created at: March 20, 2026, 1:23 p.m.