Triple

T848984

Position	Surface form	Disambiguated ID	Type / Status
Subject	GPT-2	`E18339`	entity
Predicate	trainingDataSource	`P21073`	FINISHED
Object	WebText dataset The WebText dataset is a large-scale corpus of web pages curated by OpenAI to train language models like GPT-2 on diverse, high-quality internet text.	`E99319`	NE FINISHED

How this triple was built (5 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: WebText dataset | Statement: [GPT-2, trainingDataSource, WebText dataset]

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: WebText dataset
Context triple: [GPT-2, trainingDataSource, WebText dataset]

A. Wikisource
Wikisource is a free online digital library of public domain and freely licensed texts that anyone can read and help transcribe.
B. CREA corpus
The CREA corpus is a large, authoritative reference collection of contemporary Spanish language usage compiled for linguistic and lexicographic research.
C. torchtext (ecosystem)
torchtext is a PyTorch library that provides tools, datasets, and utilities for building and processing text data in natural language processing workflows.
D. Read
Read is a surname shared by various notable individuals across fields such as politics, arts, and academia.
E. Project Gutenberg
Project Gutenberg is a pioneering digital library that offers free access to thousands of public-domain ebooks in multiple formats.
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: WebText dataset
Triple: [GPT-2, trainingDataSource, WebText dataset]

Generated description

The WebText dataset is a large-scale corpus of web pages curated by OpenAI to train language models like GPT-2 on diverse, high-quality internet text.

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: WebText dataset
Target entity description: The WebText dataset is a large-scale corpus of web pages curated by OpenAI to train language models like GPT-2 on diverse, high-quality internet text.

A. Wikisource
Wikisource is a free online digital library of public domain and freely licensed texts that anyone can read and help transcribe.
B. CREA corpus
The CREA corpus is a large, authoritative reference collection of contemporary Spanish language usage compiled for linguistic and lexicographic research.
C. torchtext (ecosystem)
torchtext is a PyTorch library that provides tools, datasets, and utilities for building and processing text data in natural language processing workflows.
D. Read
Read is a surname shared by various notable individuals across fields such as politics, arts, and academia.
E. Project Gutenberg
Project Gutenberg is a pioneering digital library that offers free access to thousands of public-domain ebooks in multiple formats.
F. None of above. chosen

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: trainingDataSource
Context triple: [GPT-2, trainingDataSource, WebText dataset]

A. trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
B. evaluationDataset
Indicates that a dataset is used as a benchmark or test set for evaluating the performance or quality of a system, model, or method.
C. trainingMethod
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
D. trainingParadigm
Indicates the specific methodological framework or approach used to train an entity (such as a model, system, or agent).
E. typicalTraining
Indicates that an entity commonly undergoes or is associated with a standard or usual form of training in relation to another entity or context.
F. None of above. chosen

Provenance (7 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_69a4938b04208190b82e1df6b572c548`	completed	March 1, 2026, 7:29 p.m.
NER	Named-entity recognition	`batch_69a4ac1fac3481909cba7070ce31a9b3`	completed	March 1, 2026, 9:14 p.m.
NED1	Entity disambiguation (via context triple)	`batch_69a792a0666c8190bfc9166d45b4e867`	completed	March 4, 2026, 2:02 a.m.
NEDg	Description generation	`batch_69a793563cc881909381f898f240c0bd`	completed	March 4, 2026, 2:05 a.m.
NED2	Entity disambiguation (via description)	`batch_69a7941add588190913198a7f7b20943`	completed	March 4, 2026, 2:08 a.m.
PD	Predicate disambiguation	`batch_69a4aa807adc8190ad808a573cf8e923`	completed	March 1, 2026, 9:07 p.m.
PDg	Predicate description generation	`batch_69a4abb157d08190a7d7281eb3f1b788`	completed	March 1, 2026, 9:12 p.m.

Created at: March 1, 2026, 7:38 p.m.