  Named entity recognition (NER) is an important task in narration extraction.
Narration, as a system of stories, provides insights into how events and
characters in the stories develop over time. This paper proposes an
architecture for NER on a corpus about social purpose organizations. This is
the first NER task specifically targeted at social service entities. We show
how this approach can be used for the sequencing of services and impacted
clients with information extracted from unstructured text. The methodology
outlines steps for extracting ontological representation of entities such as
needs and satisfiers and generating hypotheses to answer queries about impact
models defined by social purpose organizations. We evaluate the model on a
corpus of social service descriptions with empirically calculated score.
