The rapid growth of information sources on the Web has intensified the problem of data quality. In particular, the same real world entity may be described by different sources in various ways with overlapping information, and possibly conflicting or even erroneous values. In order to obtain a more complete and accurate picture for a real world entity, we need to collate the data records that refer to the entity, as well as correct any erroneous values. We observe that these two tasks are often tightly coupled: rectifying erroneous values will facilitate data collation, while linking similar records provides us with a clearer view of the data and additional evidence for error correction. In this paper, we present a framework called Comet that interleaves record linkage with error correction, taking into consideration the source reliabilities on various attributes. The proposed framework first utilizes confidence based matching to discriminate records in terms of ambiguity and source reliability. Then it performs adaptive matching to reduce the impact of erroneous values. Experiment results demonstrate that Comet outperforms the state-of-the-art techniques and is able to build complete and accurate profiles for real world entities.