Jonathon,
You might take a look at Stanford NER, which includes "person" among its list of named entities that it can identify. I believe it's been trained on a corpus of modern English texts, so uses statistical reasoning rather than a simple dictionary lookup. See
http://nlp.stanford.edu/software/CRF-NER.shtml.
For example, if you download the package, unzip it, and cd into the directory, this command (adapted from the FAQ):
$ java -mx500m -cp stanford-ner.jar edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier classifiers/english.all.3class.distsim.crf.ser.gz -textFile sample.txt -outputFormat inlineXML
will yield the following results:
The fate of <ORGANIZATION>Lehman Brothers</ORGANIZATION>, the beleaguered investment bank, hung in the balance on Sunday as <ORGANIZATION>Federal Reserve</ORGANIZATION> officials and the leaders of major financial institutions continued to gather in emergency meetings trying to complete a plan to rescue the stricken bank. Several possible plans emerged from the talks, held at the <ORGANIZATION>Federal Reserve Bank of New York</ORGANIZATION> and led by <PERSON>Timothy R. Geithner</PERSON>, the president of the <ORGANIZATION>New York Fed</ORGANIZATION>, and <ORGANIZATION>Treasury</ORGANIZATION> Secretary <PERSON>Henry M. Paulson Jr</PERSON>.
CRFClassifier tagged 85 words in 2 documents at 1075.95 words per second.
I'm not sure if anyone has integrated entity recognition as a resolver for OR, but you could pre-process your data before handing it to OR - to filter out unwanted entities like organization, etc.
Joe