Train and predict on different data structures
From what I understood from your problem, the chronicles are a way to model relationship between events and if so they are extracted from the sequential data (as a higher interpretation of data). I had a similar project involving Episode Mining in sequential database involving alarms.

The problem is whether you are trying to extract chronicles or use existent ones to predict something in a sequence (which is like using a pre-trained model).

Training a model mostly involves adjusting weights to inputs and applying (non-) linear functions on them in order to output the result in a certain format. (This is a very broad and abstract way of describing most ML algorithms)

So to answer your question, No you cannot do it in this broad way but you could try to make it so that the inputs are homogenous.

Building a  model that extract from the sequence a chronicle would help or doing the exact opposite, generating a sequence with the chronicles and then perform machine learning.


Use simple Apriori/FPGrowth to extract rules (old way of doing things)
Build a (Hidden) Markov Model to estimate each state probability, in order to predict outcome.
Use RNN to learn how a sequence behaves (LSTM or GRU) with the fact that it's time-dependent, the RNN will remember important states and forget unimportant ones (which is similar to chronicles but in a black box kind of way).
Create something hybrid or completely new.