How to pass more than 2 input columns to a Deep learning Keras model for sequence tagging/labeling

I have to build a neural network which extract relationship between two entities.Input should be: Input text/paragraph, vocabulary of entities and relationship phrases that system should recognize.

Output is sequence of tags and length of output sequence and input text/paragraph is same.

Dataset is a CSV file having 3 input columns(input text, entities in text, relationship between 2 entities) and 1 output column. I am using Keras library to build this model.

Example-input1: zomato acquires uber; input2: zomato, uber; input3: acquires ; Output: some-tag some-tag some-tag (note: these are not actual tags just an example)

I planned to use a char embedding for input text using time distributed layer with Bi-LSTM but now got stuck with 3 inputs. I am aware of keras functional api but how can I use it in a sequence tagging problem with time distributed layer. If any other approach can be used to avoid this problem, please suggest.