Complex information extraction (IE) pipelines assembled by plumbing together off-the-shelf operators, specially customized operators, and operators re-used from other text processing pipelines are becoming an integral component of most text processing frameworks. A critical task faced by the IE pipeline user is to run a post-mortem analysis on the output. Due to the diverse nature of extraction operators (often implemented by independent groups), it is time consuming and error-prone to describe operator semantics formally or operationally to a provenance system. We introduce the first system that helps IE users analyze pipeline semantics and infer provenance interactively while debugging. This allows the effort to be proportional to the need, and to focus on the portions of the pipeline under the greatest suspicion. We present a generic debugger for running post-execution analysis of any IE pipeline consisting of arbitrary types of operators. We propose an effective provenance model for IE pipelines which captures a variety of operator types, ranging from those for which full or no specifications are available. We present a suite of algorithms to effectively build provenance and facilitate debugging. Finally, we present an extensive experimental study on large-scale real-world extractions from an index of ~500 million Web documents.