You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: was constructed by Logeswaran et al. (2019) from Wikia.333https://www.wikia.com.The task is to link entity mentions in text to an entity dictionary with provided entity descriptions, in a set of domains. There are 49K, 10K, and 10K examples in the train, validation, test sets respectively.The entities in the validation and test sets are from different domains than the train set, allowing for evaluation of performance on entirely unseen entities. The entity dictionaries cover different domains and range in size from 10K to 100K entities. We also study zero-shot entity linking Logeswaran et al. (2019). Here the document setup is the same, but the knowledge base is separated in training and test time. Formally, denote \mathcal{E}_{train} and \mathcal{E}_{test} to be the knowledge base in training and test, we require \mathcal{E}_{train}\cap\mathcal{E}_{test}=\emptyset.The set of text documents, mentions, and entity dictionary are separated in training and test so that the entities being linked at test time are unseen.

Question: How we can say Wikia dataset is zero-shot dataset?
In the Wikia dataset, entities in the validation and test sets are from different domains than the train set. This setting allows the model evaluation can be done in a zero-shot setting since the set of entities are separated in training and test so that the model can't see the entities when linked at the test time.