This paper presents the NTUNLP systems in the long track and the short track of the Entity Recognition and Disambiguation Challenge 2014. We first create a dictionary that contains the possible surface forms of Freebase Ids, then scan the given text from left to right with the longest match strategy to detect the mentions, and eliminate the unwanted surface forms based on a stop word list. Methods to link to the most relevant entities and select the best candidate are proposed for these two tracks, respectively. The outside resources such as DBpedia Spotlight and TAGME are integrated to our basic NTUNLP systems. Various experimental setups are presented and discussed with the development set. In the formal run, one NTUNLP system wins the first prize in the short track and another NTUNLP system gets the fourth place in the long track.