Ever since the advent of the Semantic Web, Knowledge Graph Question Answering (KGQA) has been a niche growing subfield within AI and NLP. The task of KGQA comprises answering a Natural Language Question from using the knowledge facts present in a given RDF based Knowledge Graphs (KGs) such as DBpedia, etc. The task of entity/relation linking is a crucial sub-step while designing any KGQA solution. There are many off-the-shelf entity/relation linkers available, for example Falcon but majority of the successful KGQA solutions do not go for such off-the-shelf entity/relations linkers because precision and recall of these linkers are not very high and instead follow the semantic parsing style approaches where they use their custom-designed linkers to translate the given question into an intermediate logic-form representation which is then converted into SPARQL and executed on KG to obtain the answer. In this work, we undertake a systematic study to find whether it's possible to design an effective modular approach comprising off-the-shelf linker and seq2seq style deep neural models. Our aim is to train a deep-net based model which can predict the SPARQL query and answer for a given test question.