I've used the Stanford NLP library extensively for NER. I made heavy use of it in my senior thesis project.

It's pretty straightforward to use their library to read a document and output an XML file containing NER data (and lots of other fun stuff).

For instance, from the sentence:

> World War II, or the Second World War (often abbreviated as WWII or WW2), was a global military conflict lasting from 1939 to 1945, which involved most of the world's nations, including all of the great powers, eventually forming two opposing military alliances, the Allies and Axis.

Stanford NLP NER will output the following entities:

"World War II" - MISC "Second World War" - MISC "1939 to 1945" - DATE - NORMALIZED 1939/1945 "Axis" - MISC

You can view the output of Stanford's CoreNLP library (NER + dependency grammar + coreference resolution + some other stuff) for the Wikipedia article on World War II in my github repo:

https://raw.github.com/ryantanner/thesis/master/data/ww2samp...

edit: I should add that the real fun (for me) came from combining NER with dependency grammars and coreference resolution. It makes it very easy to turn Stanford NLP's output into a knowledge graph combining a large number of documents.