The Grounding Claims Dataset is a multi-domain dataset for evaluating whether a natural language claim is grounded (i.e., supported or entailed) by a document. The dataset is organized into four subsets, each requiring different types of reasoning:

general (1500 examples): Broad, everyday reasoning
logical (1000 examples): Logical consistency and inference
time_and_dates (100 examples): Temporal reasoning
prices_and_math (100 examples): Numerical and mathematical reasoning

Each entry consists of:

doc: A short context or passage
claim: A natural language statement to verify against the doc
label: A binary label indicating whether the claim is grounded in the document (1 for grounded, 0 for ungrounded)
dataset: The source subset name (e.g., "general")

📌 Features

Feature	Type	Description
`doc`	string	The document or passage providing the context
`claim`	string	A statement to verify against the document
`label`	string	grounded or ungrounded
`dataset`	string	The domain/subset the instance belongs to

📊 Usage

This dataset can be used to train and evaluate models on factual verification, natural language inference (NLI), and claim grounding tasks across multiple domains.