Can a sampling distribution have only 1 element or is it necessary to have multiple data elements to be a sampling distribution? Can we have a sampling distribution of only 1 realization or is it necessary to have multiple data elements to be a sampling distribution?

An example should clarify your confusion.
If we have a $N(0, 1)$ variable and draw four observations, we know that the sampling distribution of the mean is $N(0, 1/\sqrt{n}) = N(0, 1/2)$. When we actually make those four observations, we have made a single draw from $N(0, 1/2)$.
The sampling distribution is a theoretical idea, not something we observe. When we calculate the statistic, however, we are drawing a value from that theoretical distribution.
In order to generate the whole sampling distribution, we need to draw samples over and over. If you draw four observations from $N(0, 1)$, calculate the mean, and then repeat, you will wind up with many sample mean values. When you plot those values, the shape will look like $N(0, 1/2)$.
set.seed(2021)
N <- 4 # Sample size
R <- 1000 # Number of times to draw N samples from N(0, 1)
means <- rep(NA, R) 
for (i in 1:R){ # Loop R-many times

    x <- rnorm(N, 0, 1) # Draw N-many observations from N(0, 1)
    means[i] <- mean(x) # Calculate the mean of the N-many observations

}
hist(means) # Should look like N(0, 1/sqrt(N))
# Will look more like N(0, 1/sqrt(N) with larger R

