How to model the probability of a truth claim given an arrangement of eyewitness accounts supporting specific instances of that claim? Note: the title is adapted from my Philosophy.SE question Epistemic value of multiple eyewitness accounts: single event vs. multiple events given a fixed number of eyewitnesses?. See meta discussion here.

Intuitively speaking, multiple independent eyewitness accounts of a single event are more convincing than a single eyewitness account. For example, multiple independent eyewitness accounts of a loud explosion in a remote area (e.g. from different locations and viewpoints) are more convincing/reliable than a single account (e.g. maybe the single witness hallucinated the explosion).
A bit more formally, if we define X as some truth claim about some event, process or phenomenon in the real world, we could say that:
P(X is true | multiple eyewitness accounts) > P(X is true | a single eyewitness account)
However, what happens if we keep the number of eyewitness accounts constant and only change the number of events?
For example, let X = "alien abductions are real", and let Ei be a concrete example of an (alleged) alien abduction. X is a general claim, Ei is a claim about a very specific instance of X. It is clear that Ei entails X. Thus, which of the following probabilities is greater than the others?

*

*P(E1 is true | N eyewitness accounts for E1)

*P(E1 is true or E2 is true or ... or EN is true | one eyewitness account for Ei, for each i in {1, ..., N})

*P(E1 is true or E2 is true or ... or EN/2  is true | two eyewitness accounts for Ei, for each i in {1, ..., N/2})

*P(E1 is true  or E2 is true or ... or EN/3 is true | three eyewitness accounts for Ei, for each i in {1, ..., N/3})

*Etc.

In other words, given N eyewitness accounts, what is the optimal distribution of eyewitnesses over specific alleged instances of X that maximizes the probability of X being true? What should be more convincing, 1000 eyewitness accounts for E1, 500 eyewitness accounts for E1 + 500 eyewitness accounts for E2, etc.?
Notice that I used eyewitness reports of abductions by aliens as an illustrative example, but the reasoning can be extended to other rare events, such as reports of miracles, paranormal phenomena, angelic encounters, Bigfoot sightings, testimonials from whistleblowers (conspiracy theories), etc.

There’s a trade off based on the correlations among observers, with a possible quantitative argument and a clearer qualitative case.
A quantitative approach might consider $n$ observers in $k$ correlated groups, where the correlations relate the probability of an event observed by A and B with the probability of an event observed by A times the probability of an event observed by B. Then you can answer the question numerically if you can estimate the correlations within groups and the correlations between groups.
More qualitatively, suppose many people report a UFO at a particular time, but all from one neighborhood, all wearing similar eyewear, all consuming the same news source that mentions certain topics, all subject to similar reactions from friends and family and colleagues. In that case, even a large number of reports might not be convincing;  reports from a more diverse group might be more persuasive.
On this logic, should you be more persuaded that an event happened if A and B report the same event, or if they reported different events?
You may consider A and B so correlated that you find an event roughly equally likely whether one or both report it. In that case you should be more convinced an event occurred when they report separate events.
Or, you may consider A and B so different that their agreement helps overcome your background skepticism. In that case you should be more convinced an event occurred when they report the same event.
The optimal number of groups in the question will likewise depend on your evaluations of the different observers and their correlations.
