1. A data classification system, comprising: an input interface configured to receive documents comprising line-item data entries, at least some of the line-item data entries having associated attributes represented directly in the documents; a non-transitory computer readable storage medium comprising a data warehouse configured to store curated and classified data elements; a model stack including (a) at least one classification model, and (b) at least one confidence model, the classification model(s) and the confidence model(s) being independent from one another, the model stack having associated therewith (c) a related multi-level taxonomy of classifications applicable to line-item data entries included in documents received via the input interface; and processing resources including at least one processor and a memory, the processing resources being configured to at least: execute each classification model from the model stack to associate the line-item data entries included in the documents received via the input interface with potential classifications at each level in the related multi-level taxonomy; execute each confidence model from the model stack to assign probabilities of correctness for each potential classification generated by execution of the classification model(s); compare, for each of the line-item data entries included in the documents received via the input interface, output from the executed confidence model(s) to a threshold value of a probability of correctness to determine a most granular level of potential classification; designate a classification result corresponding to the determined most granular level of potential classification for each of the line-item data entries included in the documents received via the input interface; store each line-item data entry, with or without additional processing, to the data warehouse, along with an indication of its associated classification result; and respond, with records from the data warehouse, to queries from a computer terminal.