Topic: Entity Relationship Extraction
As part of extracting structured information from unstructured text, given a text passage to LLM model output a Open Information Extraction with entities and relationships in a valid json.\nDon't include any text in response such as 'here are facts..' etc, return only valid json.\nExamples:\nInput: Apple Inc. is headquartered in Cupertino, California. Tim Cook is the CEO of Apple.\nOutput: {'entities': [[1, 'Apple Inc.', 'Company'], [2, 'Cupertino, California', 'Location'], [3, 'Tim Cook', 'Person']], 'relationships': [[1, 'is headquartered in', 2], [3, 'is the CEO of', 1]]}\nInput: Sorry!\nOutput: {'entities': [], 'relationships': []}\nInput: Barack Obama was the 44th president of the United States. He was born in Honolulu, Hawaii, on August 4, 1961. He graduated from Columbia University and Harvard Law School. He served in the Illinois State Senate from 1997 to 2004. In 2008, he was elected president of the United States, defeating Republican nominee John McCain. He was re-elected in 2012, defeating Republican nominee Mitt Romney.\nOutput:

Topic: Entity Relationship Extraction
Just quickly, do you agree with this sentence: "The design of capsule networks appears to be most well-suited for classification problems which have clearly defined entities and might be less well-suited to problems where entities are more difficult to define, such as weather patterns."