Modern Knowledge Graphs (KG) often suffer from an incompleteness issue (i.e., missing facts). By representing a fact as a triplet linking two entities and via a relation , existing KG completion approaches mostly consider a link prediction task to solve this problem, i.e., given two elements of a triplet predicting the missing one, such as . However, this task implicitly has a strong yet impractical assumption on the two given elements in a triplet, which have to be correlated, resulting otherwise in meaningless predictions, such as (, , ?). Against this background, this paper studies an instance completion task suggesting - pairs for a given , i.e., . Inspired by the human psychological principle “fast-and-slow thinking”, we propose a two-step schema-aware approach RETA++ to efficiently solve our instance completion problem. It consists of two components: a RETA-Filter efficiently filtering candidate - pairs schematically matching the given , and a RETA-Grader leveraging a KG embedding model scoring each candidate - pair considering the plausibility of both the input triplet and its corresponding schema. RETA++ systematically integrates them by training RETA-Grader on the reduced solution space output by RETA-Filter via a customized negative sampling process, so as to fully benefit from the efficiency of RETA-Filter in solution space reduction and the deliberation of RETA-Grader in scoring candidate triplets. We evaluate our approach against a sizable collection of state-of-the-art techniques on three real-world KG datasets. Results show that RETA-Filter can efficiently reduce the solution space for the instance completion task, outperforming best baseline techniques by 10.61%-84.75% on the reduced solution space size, while also being 1.7x-29.6x faster than these techniques. Moreover, RETA-Grader trained on the reduced solution space also significantly outperforms the best state-of-the-art techniques on the instance completion task by 31.90%-105.02%.