Extract rows that have duplicates for certain column but are unique in another column

I'm am doing some coding in R. I would want to show the rows that have duplicates for columns ID and NAME but have different values for AGE.

For example I have this table:

ID |    NAME | AGE
111|     Mark| 22
222|     Anne| 21
333|    Chery| 30
444|    Megan| 16
555|  Charles| 37
111|     Mark| 23
222|     Anne| 22
333|    Chery| 30
111|     Mark| 22

As of now I have this code:

readfile <- read.csv(file='/home/user/shane/names.csv')
dat <- data.frame(ID=c(readfile$ID),NAME=c(readfile$NAME),AGE=c(readfile$AGE))
nam <- duplicated(dat[,c('ID','NAME)]) | duplicated(dat[,c('ID','NAME], fromLast = TRUE)
readfile[nam,]

The output looks like this:

ID |    NAME | AGE
111|     Mark| 22
222|     Anne| 21
333|    Chery| 30
111|     Mark| 23
222|     Anne| 22
333|    Chery| 30
111|     Mark| 22

I would want the output to be:

ID |    NAME | AGE
111|     Mark| 22
222|     Anne| 21
111|     Mark| 23
222|     Anne| 22
111|     Mark| 22

I would want to remove the columns with the ID = 333 as they have the same value in Age. would anyone have a suggestion?