Traditional Information Extraction (IE) takes a relation name and hand-tagged examples of that relation as input. Open IE is a relationindependent extraction paradigm that is tailored to massive and heterogeneous corpora such as theWeb. An Open IE system extracts a diverse set of relational tuples from text without any relation-specific input. How is Open IE possible? We analyze a sample of English sentences to demonstrate that numerous relationships are expressed using a compact set of relation-independent lexico-syntactic patterns, which can be learned by an Open IE system. What are the tradeoffs between Open IE and traditional IE? We consider this question in the context of two tasks. First, when the number of relations is massive, and the relations themselves are not pre-specified, we argue that Open IE is necessary. We then present a new model for Open IE called O-CRF and show that it achieves increased precision and nearly double the recall than the model employed by TEXTRUNNER, the previous stateof-the-art Open IE system. Second, when the number of target relations is small, and their names are known in advance, we show that O-CRF is able to match the precision of a traditional extraction system, though at substantially lower recall. Finally, we show how to combine the two types of systems into a hybrid that achieves higher precision than a traditional extractor, with comparable recall.