There are alot of clever super-algorithm startegies for parsing wikipedia, but there is lots of long-tail data that no algorithm can understand, and can only be parsed ad-hoc by directed, supervised work by humans. These are tools for this task.
for tools to add the structured data to freebase, see these mql-making scripts

wikipedia category digger

List the members of a wikipedia category Wikipedia category: (eg. Category:Rock and Roll Hall of Fame inductees )
depth:
Output:
api - http://www.spencerwaterbed.com/soft/hugger/dig.php?cat=Dams in Canada&deep=2

wikipedia list parser

Parse a bulleted list in a wikipedia article
Wikipedia article:(eg. List of cancer types)
Output:

wikipedia template parser

Parse a wikipedia template
Wikipedia category:(eg. Category:Hydroelectric power plants in Iraq )
depth: variable
Output:

wikipedia table parser

To parse a table in a wikipedia article, open a google spreadsheet and paste into a cell =ImportHtml("http://en.wikipedia.org/wiki/List_of_UFO_sightings", "table",6)

wikipedia year from title

get an event's year from its title
match years between: - Wikipedia category: (eg. Category:Explosions in Canada )
depth: Output:

Soft date mining

Script to grab an event's date, with the assumtion it's the first mentioned date in the article's introduction.
match years between: - Wikipedia category: (eg. Category:Explosions in Canada )
depth: Output:

Predicate matcher

Not a dazzling nlp tool, but simple text matches and smart filtering can get alot of accurate data.
example: (sponsored by *)
choose filters

Wikipedia category: (eg. Category:Skateboarders )
depth:
still beta. still beta.still beta.
source