How can I tell the difference between binned vs unbinned maximum likelihood fit?

I know that this is a very basic question, but I keep getting confused between the two. I want to clear this once and for all!

For example

TH1F* hist = new TH1F("hist", "Jpsi_Inv_mass Distribution", 100, 3.04, 3.15);

in this , if I fit this distribution normally using roofit will it be unbinned maximum likelihood fit or Binned maxmium likelihood fit (because I divided it into 100 bins)?

I have indeed searched the forum, but I am still a bit confused on this. Like if we divide the histogram in bins ( like above line) do we call it binned ml fit, and when we directly try to fit the data points without any binning, it is unbinned ml fit?

What is the easiest way we can distinguish between the two?

Yes, this is correct, if you fit an histogram the data are binned and then the corresponding fit is binned, instead in the case of fitting directly the data points the fit is unbinned.
If you ask to ChatGPT or you google for this, you will get very detailed answer to your questions
Best regards
Lorenzo