In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said. 

Example Input: Sentence: Everyone really loved the oatmeal cookies; only a few people liked the snickerdoodle cookies. Next time, we should make fewer of them. Question: Which cookie should we make fewer of, next time?
Example Output: oatmeal

Example Input: Sentence: Dan had to stop Bill from toying with the injured bird. He is very cruel. Question: Who is cruel?
Example Output: Dan

Example Input: Sentence: I stuck a pin through a carrot. When I pulled the pin out, it left a hole. Question: What left a hole?
Example Output:
carrot