Where does Apple's predictive keyboard get its "contextual" content from?
If you read the Apple patents there is a explanation how it works.

US patents

Patent No. 8,232,973 for a "Method, device, and graphical user interface providing word recommendations for text input"

and 

Apple’s U.S Patent No. 8,074,172 for a "method, system, and graphical user interface for providing word recommendations" or predictive text.


  …..However, the size of these portable communication devices also
  restricts the size of the text input device, such as a physical or
  virtual keyboard, in the portable device. With a size-restricted
  keyboard, designers are often forced to make the keys smaller or
  overload the keys. Both may lead to typing mistakes and thus more
  backtracking to correct the mistakes. This makes the process of
  communication by text on the devices inefficient and reduces user
  satisfaction with such portable communication devices.  
  
  .....The set of strings are compared against a dictionary. Words in the
  dictionary that have any of the set of strings as a prefix are
  identified (206). As used herein, "prefix" means that the string is a
  prefix of a word in the dictionary or is itself a word in the
  dictionary. A dictionary, as used herein, refers to a list of words.
  The dictionary may be pre-made and stored in the memory. The
  dictionary may also include usage frequency rankings for each word in
  the dictionary. A usage frequency ranking for a word indicates (or
  more generally, corresponds to) the statistical usage frequency for
  that word in a language. In some embodiments, the dictionary may
  include different usage frequency rankings for different variants of a
  language. For example, a dictionary of words in the English language
  may have different usage frequency rankings with respect to American
  English and British English.  
  
  In some embodiments, the dictionary may be customizable. That is,
  additional words may be added to the dictionary by the user.
  Furthermore, in some embodiments, different applications may have
  different dictionaries with different words and usage frequency
  rankings. For example, an email application and an SMS application may
  have different dictionaries, with different words and perhaps
  different usage frequency rankings within the same language. 
  
  The identified words are the candidate words that may be presented to
  the user as recommended replacements for the input sequence. The
  candidate words are scored (208). Each candidate word is scored based
  on a character-to-character comparison with the input sequence and
  optionally other factors. Further details regarding the scoring of
  candidate words are described below, in relation to FIGS. 3 and 7A-7C.
  A subset of the candidate words are selected based on predefined
  criteria (210) and the selected subset is presented to the user (212).
  In some embodiments, the selected candidate words are presented to the
  user as a horizontal listing of words.


Graphical view:



I did not intend to provide a complete explanation on how it works, but provide a guide to it.

So what about 



Notice my dictionary does not have it so it is red underlined and it is recommending to look it up.

The choices are:

1- look it up and correct

2- add to dictionary as typed

3- ignore it

The predictive keyboard logic will take in account all 3 inputs. Even the ignored version, and it will assume it is what I wanted. So in your case, you probably did not add it to your dictionary, but used that word more than once, so it became marked as the most probable (predictive).