Flexible entity resolution networks

Abstract

Among other techniques, techniques for machine learning-based entity resolution are described. An example method includes receiving an entity resolution request, the entity resolution request indicating a first entity and a second entity; identifying a plurality of first attributes in a data model; identifying a plurality of second attributes in the data model; creating a first string based on the plurality of first attributes of the data model; creating a second string based on the plurality of second attributes of the data model; generating a first prompt based on the first string; generating a second prompt based on the second string; providing the first prompt to a domain-agnostic large language model; generating, by the domain-agnostic large language model using the first prompt, a first domain-agnostic large language model result; clipping the first domain-agnostic large language model result; providing the second prompt to the domain-agnostic large language model; generating, by the domain-agnostic large language model using the second prompt, a second domain-agnostic large language model result; clipping the second domain-agnostic large language model result; generating, by a downstream neural network classifier, a machine learning final result based on the clipped first and second domain-agnostic large language model result; and merging, based on the machine learning final result, the first entity and the second entity.