11. A system, comprising: one or more processors; a memory device operatively coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the system 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.
11. A system, comprising: one or more processors; a memory device operatively coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the system 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. 18. The system of claim 11 , wherein the stored instructions, when executed by the one or more processors, further cause to the system to perform functions that comprise: persisting the entity data structures; performing random sampling algorithm functions based on predetermined similarity comparisons and intelligent shuffling between new incoming data and the persisted entities from previous runs; and creating new entities on an as-needed basis for further runs.