Roasted Coffee Product Catalog Evidence Retrieval Dataset

#evidence retrieval #product attribute retrieval #source-grounded question answering #roasted coffee product search #coffee catalog attribute lookup #e-commerce product information verification

5000
0.3GB
JSON
CATL
MOBIUSI INC

Updated:2026-10-25

AI Analysis & Value Prop

This dataset contains roasted coffee catalog entries, source excerpts with product information, and evidence annotations for product attribute questions, covering catalog details such as product names, roast levels, package sizes, and flavor descriptions. It also includes retrieval queries, normalized product attributes, evidence spans, verification results, and answerability labels for building and evaluating RAG systems that retrieve supporting catalog evidence. The data helps connect product attribute answers to specific source material and supports coffee catalog question answering and retrieval quality evaluation.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
catalog_entry	object	The original catalog details for the roasted coffee product.
evidence_spans	array	Evidence spans aligned to the source excerpt and derived from the evidence annotation.
source_excerpt	string	The original source text containing product information that may support an answer.
retrieval_query	string	A normalized retrieval query derived from the product details and attribute question.
evidence_verified	boolean	Whether the annotated evidence quote can be matched to the source excerpt.
attribute_question	string	The question about a product attribute that the evidence should support.
answerability_label	boolean	Whether the source excerpt provides sufficient evidence to answer the attribute question.
evidence_annotation	object	The original annotation identifying the attribute and relevant supporting evidence.
normalized_product_attributes	object	Product attributes standardized from the original catalog entry for consistent retrieval and evaluation.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What does the dataset contain?: It contains roasted coffee catalog entries, source text excerpts, product attribute questions, evidence annotations, and retrieval queries. It also provides normalized attributes, evidence spans, evidence verification results, and answerability labels.

Which systems is this dataset suitable for?: It is suitable for training or evaluating retrieval-augmented generation systems, especially for testing whether they can retrieve relevant source evidence for coffee product attribute questions, assess evidence sufficiency, and link answers to their sources.

How can I access the data, and what are the licensing terms?: Refer to the dataset product page for access methods, file specifications, and license terms. Review the permitted usage before applying the data to training, evaluation, or research.

Does the dataset include evidence quality labels?: Yes. Evidence verification and answerability labels indicate whether annotated evidence can be matched to a source excerpt and whether the excerpt provides enough information to answer the corresponding product attribute question.

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Cite this Work

@dataset{Mobiusi2026,
  title={Roasted Coffee Product Catalog Evidence Retrieval Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/922dcee34060fecc89de9739e63ae37f},
  urldate={2026-10-20},
  keywords={roasted coffee dataset,coffee product catalog,evidence retrieval dataset,RAG evaluation data,coffee attribute QA,source evidence annotation,retrieval augmented generation,product information verification},
  version={1.0}
}

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