Triple

T4804631
Position Surface form Disambiguated ID Type / Status
Subject GPT series E106918 entity
Predicate hasMember P10 FINISHED
Object GPT-4o
GPT-4o is an advanced multimodal large language model in the GPT series capable of understanding and generating text, images, and other data types with high efficiency.
 E472107 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-4o | Statement: [GPT series, hasMember, GPT-4o]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GPT-4o
Context triple: [GPT series, hasMember, GPT-4o]
  • A. GPT-4
    GPT-4 is a large multimodal language model known for its advanced reasoning, comprehension, and generation capabilities across text and images.
  • B. ChatGPT
    ChatGPT is an advanced conversational AI model developed by OpenAI that can understand and generate human-like text across a wide range of topics and tasks.
  • C. 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.
  • D. 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.
  • E. 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.
  • 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-4o
Triple: [GPT series, hasMember, GPT-4o]
Generated description
GPT-4o is an advanced multimodal large language model in the GPT series capable of understanding and generating text, images, and other data types with high efficiency.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: GPT-4o
Target entity description: GPT-4o is an advanced multimodal large language model in the GPT series capable of understanding and generating text, images, and other data types with high efficiency.
  • A. GPT-4
    GPT-4 is a large multimodal language model known for its advanced reasoning, comprehension, and generation capabilities across text and images.
  • B. ChatGPT
    ChatGPT is an advanced conversational AI model developed by OpenAI that can understand and generate human-like text across a wide range of topics and tasks.
  • C. 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.
  • D. 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.
  • E. 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.
  • 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_69be4d9e1ea88190b098d5203bd1d145 completed March 21, 2026, 7:49 a.m.
NEDg Description generation batch_69be4e61a9a8819096e3c4ba7612de85 completed March 21, 2026, 7:53 a.m.
NED2 Entity disambiguation (via description) batch_69be4f0d51ac819097636aaf2429f317 completed March 21, 2026, 7:55 a.m.
Created at: March 20, 2026, 1:23 p.m.