21. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause a computer to perform functions that comprise: ingesting text data from a plurality of documents containing a plurality of mentions; locating, from the text data, for each of a selected plurality of chains of coreferent mentions, a particular context-based name from the respective chain, wherein the coreferent mentions correspond to entities and the context-based name is a longest name in the respective chain, a last name in the respective chain, or a most frequently occurring name in the respective chain; determining an entity category for each respective one of the plurality of chains; determining one or more entity attributes from structured data and unstructured data; based on the located particular context-based name, the entity category, and the one or more attributes, assigning high-probability coreferent chains to high-confidence buckets, such as to produce a power law probability distribution having a head region and a tail region; and resolving, based at least in part on the power law probability distribution, the coreferent mentions to identify corresponding real-world entities.
21. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause a computer to perform functions that comprise: ingesting text data from a plurality of documents containing a plurality of mentions; locating, from the text data, for each of a selected plurality of chains of coreferent mentions, a particular context-based name from the respective chain, wherein the coreferent mentions correspond to entities and the context-based name is a longest name in the respective chain, a last name in the respective chain, or a most frequently occurring name in the respective chain; determining an entity category for each respective one of the plurality of chains; determining one or more entity attributes from structured data and unstructured data; based on the located particular context-based name, the entity category, and the one or more attributes, assigning high-probability coreferent chains to high-confidence buckets, such as to produce a power law probability distribution having a head region and a tail region; and resolving, based at least in part on the power law probability distribution, the coreferent mentions to identify corresponding real-world entities. 23. The non-transitory computer-readable medium of claim 21 , wherein assigning the high-probability chains to the high-confidence buckets comprises: grouping the plurality of chains based on the respective context-based name and category such that chains having a same context-based name and same category are grouped together into a respective partition; within the respective partition, grouping chains that correspond to the same concept into sub-entities; and grouping together, across and within partitions, the sub-entities that correspond to the same concept.