You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: I want to learn embeddings for entities and relations in knowledge graphs.

Keyphrases: knowledge graph embedding text

Dataset description:
OLPBENCH: OLPBENCH is a large Open Link Prediction benchmark, which was derived from the state-of-the-art Open Information Extraction corpus OPIEC (Gashteovski et al., 2019). OLPBENCH contains 30M open triples, 1M distinct open relations and 2.5M distinct mentions of approximately 800K entities.
YAGO: Yet Another Great Ontology (YAGO) is a Knowledge Graph that augments WordNet with common knowledge facts extracted from Wikipedia, converting WordNet from a primarily linguistic resource to a common knowledge base. YAGO originally consisted of more than 1 million entities and 5 million facts describing relationships between these entities.
MutualFriends: In MutualFriends, two agents, A and B, each have a private knowledge base, which contains a list of friends with multiple attributes (e.g., name, school, major, etc.). The agents must chat with each other to find their unique mutual friend.
FrameNet: FrameNet is a linguistic knowledge graph containing information about lexical and predicate argument semantics of the English language. FrameNet contains two distinct entity classes: frames and lexical units, where a frame is a meaning and a lexical unit is a single meaning for a word.
FB15k: The FB15k dataset contains knowledge base relation triples and textual mentions of Freebase entity pairs. It has a total of  592,213 triplets with 14,951 entities and 1,345 relationships.
WN18: The WN18 dataset has 18 relations scraped from WordNet for roughly 41,000 synsets, resulting in 141,442 triplets. It was found out that a large number of the test triplets can be found in the training set with another relation or the inverse relation.
SNIPS: The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:   SearchCreativeWork (e.g. Find me the I, Robot television show), GetWeather (e.g.

Options:- A: OLPBENCH  B: YAGO  C: MutualFriends  D: FrameNet  E: FB15k  F: WN18  G: SNIPS
["FB15k", "WN18"]