Given a short query with keyphrases about a research idea in 2019 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: I want to fine-tune the BERT model on the WSCR dataset.

Keyphrases: winograd schema challenge natural language

Dataset description:
NumerSense: Contains 13.6k masked-word-prediction probes, 10.5k for fine-tuning and 3.1k for testing.
GLUE: General Language Understanding Evaluation (GLUE) benchmark is a collection of nine natural language understanding tasks, including single-sentence tasks CoLA and SST-2, similarity and paraphrasing tasks MRPC, STS-B and QQP, and natural language inference tasks MNLI, QNLI, RTE and WNLI.
CoarseWSD-20: The CoarseWSD-20 dataset is a coarse-grained sense disambiguation dataset built from Wikipedia (nouns only) targeting 2 to 5 senses of 20 ambiguous words. It was specifically designed to provide an ideal setting for evaluating Word Sense Disambiguation (WSD) models (e.g.
SNIPS: The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:   SearchCreativeWork (e.g. Find me the I, Robot television show), GetWeather (e.g.

Options:- A: NumerSense  B: GLUE  C: CoarseWSD-20  D: SNIPS
GLUE