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Recently cross-document integration and reconciliation of extracted information became of interest to researchers in Arabic natural language processing. Given a set of documents $A$, we use Arabic morphological analysis, finite state machines, and graph transformations to extract named entities N_a and relations R_a expressed as edges in a graph G = (N_a, R_a). We use the same techniques to extract entities N_b and relations R_b from a separate set of documents B. We use G to disambiguate N_b and R and we integrate the resulting entities back into G by annotating the nodes and edges in G with elements from N_b. We apply our approach in an iterative manner. Our results show a significant increase in accuracy from 41% to 93% after applying this cross-document NLP methodology to hadith and biography documents.