  Extraction of categorised named entities from text is a complex task given
the availability of a variety of Named Entity Recognition (NER) models and the
unstructured information encoded in different source document formats.
Processing the documents to extract text, identifying suitable NER models for a
task, and obtaining statistical information is important in data analysis to
make informed decisions. This paper presents TNNT, a toolkit that automates the
extraction of categorised named entities from unstructured information encoded
in source documents, using diverse state-of-the-art Natural Language Processing
(NLP) tools and NER models. TNNT integrates 21 different NER models as part of
a Knowledge Graph Construction Pipeline (KGCP) that takes a document set as
input and processes it based on the defined settings, applying the selected
blocks of NER models to output the results. The toolkit generates all results
with an integrated summary of the extracted entities, enabling enhanced data
analysis to support the KGCP, and also, to aid further NLP tasks.
