Instruction:
Text: Furthermore , we propose the use of standard parser evaluation methods for automatically evaluating the summarization quality of sentence condensation systems .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "evaluate for", "head": "parser evaluation methods", "tail": "summarization quality"}, {"relation": "evaluate for", "head": "summarization quality", "tail": "sentence condensation systems"}]


Instruction:
Text: An experimental evaluation of summarization quality shows a close correlation between the automatic parse-based evaluation and a manual evaluation of generated strings .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "evaluate for", "head": "summarization quality", "tail": "automatic parse-based evaluation"}, {"relation": "compare", "head": "automatic parse-based evaluation", "tail": "manual evaluation"}]


Instruction:
Text: Overall summarization quality of the proposed system is state-of-the-art , with guaranteed grammaticality of the system output due to the use of a constraint-based parser/generator .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "evaluate for", "head": "summarization quality", "tail": "system"}, {"relation": "evaluate for", "head": "grammaticality", "tail": "system"}, {"relation": "used for", "head": "constraint-based parser/generator", "tail": "system"}]


Instruction:
Text: The robust principal component analysis -LRB- robust PCA -RRB- problem has been considered in many machine learning applications , where the goal is to decompose the data matrix to a low rank part plus a sparse residual .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "robust principal component analysis -LRB- robust PCA -RRB- problem", "tail": "machine learning applications"}, {"relation": "part of", "head": "low rank part", "tail": "data matrix"}, {"relation": "conjunction", "head": "low rank part", "tail": "sparse residual"}, {"relation": "part of", "head": "sparse residual", "tail": "data matrix"}]


Instruction:
Text: While current approaches are developed by only considering the low rank plus sparse structure , in many applications , side information of row and/or column entities may also be given , and it is still unclear to what extent could such information help robust PCA .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "low rank plus sparse structure", "tail": "approaches"}, {"relation": "used for", "head": "information", "tail": "robust PCA"}]


Instruction:
Text: Thus , in this paper , we study the problem of robust PCA with side information , where both prior structure and features of entities are exploited for recovery .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "side information", "tail": "robust PCA"}, {"relation": "conjunction", "head": "prior structure", "tail": "features of entities"}, {"relation": "used for", "head": "prior structure", "tail": "recovery"}, {"relation": "used for", "head": "features of entities", "tail": "recovery"}]


Instruction:
Text: We propose a convex problem to incorporate side information in robust PCA and show that the low rank matrix can be exactly recovered via the proposed method under certain conditions .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "convex problem", "tail": "side information"}, {"relation": "part of", "head": "side information", "tail": "robust PCA"}, {"relation": "used for", "head": "method", "tail": "low rank matrix"}]


Instruction:
Text: In particular , our guarantee suggests that a substantial amount of low rank matrices , which can not be recovered by standard robust PCA , become re-coverable by our proposed method .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "method", "tail": "low rank matrices"}]


Instruction:
Text: The result theoretically justifies the effectiveness of features in robust PCA .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "feature of", "head": "features", "tail": "robust PCA"}]


Instruction:
Text: In addition , we conduct synthetic experiments as well as a real application on noisy image classification to show that our method also improves the performance in practice by exploiting side information .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "evaluate for", "head": "noisy image classification", "tail": "method"}, {"relation": "used for", "head": "side information", "tail": "method"}]


Instruction:
Text: This paper presents necessary and sufficient conditions for the use of demonstrative expressions in English and discusses implications for current discourse processing algorithms .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "feature of", "head": "demonstrative expressions", "tail": "English"}, {"relation": "used for", "head": "implications", "tail": "discourse processing algorithms"}]


Instruction:
Text: This research is part of a larger study of anaphoric expressions , the results of which will be incorporated into a natural language generation system .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "anaphoric expressions", "tail": "natural language generation system"}]


Instruction:
Text: Using the IEMOCAP database , discrete -LRB- categorical -RRB- and continuous -LRB- attribute -RRB- emotional assessments evaluated by the actors and na ¨ ıve listeners are compared .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "IEMOCAP database", "tail": "discrete -LRB- categorical -RRB- and continuous -LRB- attribute -RRB- emotional assessments"}]


Instruction:
Text: The problem of blind separation of underdetermined instantaneous mixtures of independent signals is addressed through a method relying on nonstationarity of the original signals .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "method", "tail": "blind separation of underdetermined instantaneous mixtures of independent signals"}, {"relation": "used for", "head": "nonstationarity", "tail": "method"}]


Instruction:
Text: In comparison with previous works , in this paper it is assumed that the signals are not i.i.d. in each epoch , but obey a first-order autoregressive model .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "first-order autoregressive model", "tail": "signals"}]


Instruction:
Text: This model was shown to be more appropriate for blind separation of natural speech signals .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "model", "tail": "blind separation of natural speech signals ."}]


Instruction:
Text: A separation method is proposed that is nearly statistically efficient -LRB- approaching the corresponding Cramér-Rao lower bound -RRB- , if the separated signals obey the assumed model .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "feature of", "head": "Cramér-Rao lower bound -RRB-", "tail": "separation method"}]


Instruction:
Text: In the case of natural speech signals , the method is shown to have separation accuracy better than the state-of-the-art methods .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "method", "tail": "natural speech signals"}, {"relation": "compare", "head": "method", "tail": "methods"}, {"relation": "evaluate for", "head": "separation accuracy", "tail": "method"}, {"relation": "evaluate for", "head": "separation accuracy", "tail": "methods"}, {"relation": "used for", "head": "methods", "tail": "natural speech signals"}]


Instruction:
Text: This paper proposes to use a convolution kernel over parse trees to model syntactic structure information for relation extraction .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "convolution kernel over parse trees", "tail": "syntactic structure information"}, {"relation": "used for", "head": "syntactic structure information", "tail": "relation extraction"}]


Instruction:
Text: Our study reveals that the syntactic structure features embedded in a parse tree are very effective for relation extraction and these features can be well captured by the convolution tree kernel .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "feature of", "head": "syntactic structure features", "tail": "parse tree"}, {"relation": "used for", "head": "syntactic structure features", "tail": "relation extraction"}, {"relation": "used for", "head": "convolution tree kernel", "tail": "features"}]


Instruction:
Text: Evaluation on the ACE 2003 corpus shows that the convolution kernel over parse trees can achieve comparable performance with the previous best-reported feature-based methods on the 24 ACE relation subtypes .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "evaluate for", "head": "ACE 2003 corpus", "tail": "convolution kernel over parse trees"}, {"relation": "compare", "head": "feature-based methods", "tail": "convolution kernel over parse trees"}]


Instruction:
Text: It also shows that our method significantly outperforms the previous two dependency tree kernels on the 5 ACE relation major types .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "compare", "head": "method", "tail": "dependency tree kernels"}]


Instruction:
Text: This paper presents the results of automatically inducing a Combinatory Categorial Grammar -LRB- CCG -RRB- lexicon from a Turkish dependency treebank .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "part of", "head": "Combinatory Categorial Grammar -LRB- CCG -RRB- lexicon", "tail": "Turkish dependency treebank"}]


Instruction:
Text: The fact that Turkish is an agglutinating free word order language presents a challenge for language theories .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "hyponym of", "head": "Turkish", "tail": "agglutinating free word order language"}]


Instruction:
Text: We explored possible ways to obtain a compact lexicon , consistent with CCG principles , from a treebank which is an order of magnitude smaller than Penn WSJ .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "part of", "head": "compact lexicon", "tail": "treebank"}, {"relation": "compare", "head": "treebank", "tail": "Penn WSJ"}]


Instruction:
Text: While sentence extraction as an approach to summarization has been shown to work in documents of certain genres , because of the conversational nature of email communication where utterances are made in relation to one made previously , sentence extraction may not capture the necessary segments of dialogue that would make a summary coherent .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "sentence extraction", "tail": "summarization"}]


Instruction:
Text: In this paper , we present our work on the detection of question-answer pairs in an email conversation for the task of email summarization .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "detection of question-answer pairs", "tail": "email summarization"}, {"relation": "used for", "head": "email conversation", "tail": "detection of question-answer pairs"}]


Instruction:
Text: We show that various features based on the structure of email-threads can be used to improve upon lexical similarity of discourse segments for question-answer pairing .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "features", "tail": "lexical similarity"}, {"relation": "used for", "head": "features", "tail": "question-answer pairing"}, {"relation": "used for", "head": "structure of email-threads", "tail": "features"}, {"relation": "feature of", "head": "lexical similarity", "tail": "discourse segments"}]


Instruction:
Text: Specifically , we show how to incorporate a simple prior on the distribution of natural images into support vector machines .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "prior on the distribution of natural images", "tail": "support vector machines"}]


Instruction:
Text: SVMs are known to be robust to overfitting ; however , a few training examples usually do not represent well the structure of the class .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "SVMs", "tail": "overfitting"}]


Instruction:
Text: Our experiments on real data sets show that the resulting detector is more robust to the choice of training examples , and substantially improves both linear and kernel SVM when trained on 10 positive and 10 negative examples .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "evaluate for", "head": "real data sets", "tail": "detector"}, {"relation": "compare", "head": "detector", "tail": "linear and kernel SVM"}]


Instruction:
Text: Although the study of clustering is centered around an intuitively compelling goal , it has been very difficult to develop a unified framework for reasoning about it at a technical level , and profoundly diverse approaches to clustering abound in the research community .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "unified framework", "tail": "reasoning"}]


Instruction:
Text: Relaxations of these properties expose some of the interesting -LRB- and unavoidable -RRB- trade-offs at work in well-studied clustering techniques such as single-linkage , sum-of-pairs , k-means , and k-median .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "hyponym of", "head": "single-linkage", "tail": "well-studied clustering techniques"}, {"relation": "conjunction", "head": "single-linkage", "tail": "sum-of-pairs"}, {"relation": "hyponym of", "head": "sum-of-pairs", "tail": "well-studied clustering techniques"}, {"relation": "conjunction", "head": "sum-of-pairs", "tail": "k-means"}, {"relation": "hyponym of", "head": "k-means", "tail": "well-studied clustering techniques"}, {"relation": "conjunction", "head": "k-means", "tail": "k-median"}, {"relation": "hyponym of", "head": "k-median", "tail": "well-studied clustering techniques"}]


Instruction:
Text: With relevant approach , we identify important contents by PageRank algorithm on the event map constructed from documents .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "PageRank algorithm", "tail": "relevant approach"}, {"relation": "used for", "head": "event map", "tail": "PageRank algorithm"}, {"relation": "used for", "head": "documents", "tail": "event map"}]


Instruction:
Text: After returning to the U.K. she attended the independent Upper Chine School in Shanklin on the  Isle of Wight  which subsequently merged with the independent Ryde School and was renamed  Ryde School with Upper Chine  ) .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "Isle of Wight", "tail": "Ryde School with Upper Chine"}]


Instruction:
Text: Supported by their own buying staff  Mars  purchases produce and seafood daily from local markets and always includes local produce when available from  Maryland  growers .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "Mars", "tail": "Maryland"}]


Instruction:
Text: The California Department of Alcohol and Drug Programs ( ADP ) is a  California  state agency concerned with  substance abuse  prevention and treatment .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "California", "tail": "substance abuse"}]


Instruction:
Text: But but  Aetna  's headquarters are in  Connecticut  with that great Senator Joe Lieberman .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "Aetna", "tail": "Connecticut"}]


Instruction:
Text: Singapore Airlines  (  SIA  ) said Wednesday it would begin flying the world's biggest passenger plane the Airbus A380 on its Melbourne route in late September .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "Singapore Airlines", "tail": "SIA"}]


Instruction:
Text: He attended  St. Benet Biscop Catholic High School  in Bedlington  Northumberland  .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "St. Benet Biscop Catholic High School", "tail": "Northumberland"}]


Instruction:
Text: The  Sacramento Heatwave  is an American Basketball Association ( ABA ) team based in Sacramento  California  .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "Sacramento Heatwave", "tail": "California"}]


Instruction:
Text: In 2010  Oklahoma  City-based Love's Travel Stops Country Stores ranked 18th on the Forbe's list of largest private companies Tulsa-based  QuikTrip  ranked 37th and Oklahoma City-based Hobby Lobby ranked 198th in 2010 report .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "Oklahoma", "tail": "QuikTrip"}]


Instruction:
Text: DigiPen Institute of Technology  is a for-profit college located in Redmond  Washington  .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "DigiPen Institute of Technology", "tail": "Washington"}]


Instruction:
Text: Viva  is the brand name for the  York Region  Rapid Transit Plan and was funded through a Public-Private Partnership ( P3 ) consortium called the York Region Rapid Transit Corporation .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "Viva", "tail": "York Region"}]


Instruction:
Text: The 648th MEB is one of the two newest brigades placed in  Georgia  in 2007 ; the other being Ellenwood's 560th Battlefield Surveillance Brigade at Fort Gillem the  Army National Guard  's first battlefield surveillance brigade which was stood up in October of 2007 then officially activated January 2009 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "Georgia", "tail": "Army National Guard"}]


Instruction:
Text: THF Realty  Inc. used  Missouri  's tax program known as tax increment financing to build what is said to be the largest strip mall in the country on land in the Chesterfield Valley area of St. Louis County that was submerged in the floods of 1993 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "THF Realty", "tail": "Missouri"}]


Instruction:
Text: Distance Diagnostics through Digital Imaging ( DDDI ) is the name of a system developed at the  University of Georgia College of Agricultural and Environmental Sciences  to allow textual information and descriptive images to be submitted directly from  Georgia  county Extension offices for rapid diagnosis of plant and pest disease issues by resource professionals at the University .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "University of Georgia College of Agricultural and Environmental Sciences", "tail": "Georgia"}]


Instruction:
Text: The play was initially banned but was allowed to premiere October 24 1905 at the  Moscow  Art Theater with Vasili Kachalov as Professor Protassov and  Olga Knipper  ( wife of Anton Chekhov ) as Lisa .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "cities of residence", "head": "Moscow", "tail": "Olga Knipper"}]


Instruction:
Text: Sydenham Institute of Management Studies  Research and Entrepreneurship Education (  SIMSREE  ) is one of the Premiere Management Institute of the country imparting Management Studies under University of Mumbai named after the then governor of Bombay Lord Sydenham of Combe in 1913 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "Sydenham Institute of Management Studies", "tail": "SIMSREE"}]


Instruction:
Text: Ottawa is governed by the 24-member  Ottawa  City Council consisting of 23 councillors each representing one ward and the mayor currently  Jim Watson  elected in a citywide vote .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "cities of residence", "head": "Ottawa", "tail": "Jim Watson"}]


Instruction:
Text: He was 37 years old and an acting captain in the 3rd Battalion The  Durham Light Infantry   British Army  attached to 14th Battalion during the First World War when the following deed took place for which he was awarded the VC .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "Durham Light Infantry", "tail": "British Army"}]


Instruction:
Text: After his former bandmates moved back to  Salvador   Raul  made a living as an English teacher before being hired by CBS still in 1968 as creative director and record producer .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "cities of residence", "head": "Salvador", "tail": "Raul"}]


Instruction:
Text: After  BCC  went bankrupt after the recession of 1969 70 the  BCC-500  was transferred to the University of Hawaii where it continued in use through the 1970s .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "BCC", "tail": "BCC-500"}]


Instruction:
Text: His  most famous and popular alter ego Slim  Shady  originated from The Slim Shady EP .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "His", "tail": "Shady"}]


Instruction:
Text: Singapore Airlines  (  SIA  ) said Wednesday it will change the engines of 3 Airbus A380 planes following a mid-air engine failure on a Qantus-operated superjumbo .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "Singapore Airlines", "tail": "SIA"}]


Instruction:
Text: The Malaysian government also established Kolej Serdang which became the  Universiti Pertanian Malaysia  (  UPM  ) in the 1970s to train agricultural and agroindustrial engineers and agribusiness graduates to conduct research in the field .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "Universiti Pertanian Malaysia", "tail": "UPM"}]


Instruction:
Text: It was rulled by Jat ruler Maharaja Narendra Singh ( 1845 1862 ) fortified the city of  Patiala  by constructing ramparts and ten gates around the city : The royal house is now headed by His Highness Maharajadhiraj Captain  Amarinder Singh  Mahendra Bahadur of Patiala who also served as the Chief Minister of Punjab from 2002 to 2007 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "cities of residence", "head": "Patiala", "tail": "Amarinder Singh"}]


Instruction:
Text: Presidential Palace Historical Site ( Vietnamese : Khu di Ph? Ch? t?ch ) which is located in Hanoi  V  ietnam is the place  where Ho Ch  i Minh lived and worked in most of his revolutionary life ( from December 19 1954 to September 2 1969 ) .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "cities of residence", "head": "V", "tail": "where Ho Ch"}]


Instruction:
Text: University  of  Turku  has been involved in many research projects in the Peruvian Amazon .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "University", "tail": "Turku"}]


Instruction:
Text: Meanwhile a series of preparatory meetings for the summit began on Friday at the  EAC  secretariat in  Arusha  Municipality .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "EAC", "tail": "Arusha"}]


Instruction:
Text: The 2012  Bryant Bulldogs  football team represented  Bryant University  in the 2012 NCAA Division I FCS football season .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "members", "head": "Bryant Bulldogs", "tail": "Bryant University"}]


Instruction:
Text: James A. Harding and David Lipscomb founded the  Nashville  Bible School which is now known as  Lipscomb University  in honor of the latter .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "Nashville", "tail": "Lipscomb University"}]


Instruction:
Text: During his stay at Vandenberg he attended classes and earned a Master of Public Administration degree from  Golden Gate University  in nearby San Francisco  California  .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "Golden Gate University", "tail": "California"}]


Instruction:
Text: While at  North Carolina State University  Thacker won the  NCAA  heavyweight wrestling championship in 1984 collecting 4 Atlantic Coast Conference titles along with the Wolfpack .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "members", "head": "North Carolina State University", "tail": "NCAA"}]


Instruction:
Text: After the War the Triborough Bridge Authority was merged with the  Tunnel Authority  allowing the new  Triborough Bridge and Tunnel Authority  ( TBTA ) to take over the project .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "Tunnel Authority", "tail": "Triborough Bridge and Tunnel Authority"}]


Instruction:
Text: Aberdeen  police records show that  Cobain  was arrested for spray painting the phrase Ai n't got no how watchamacallit on other vehicles .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "cities of residence", "head": "Aberdeen", "tail": "Cobain"}]


Instruction:
Text: Among the other songs on the album was a cover of a chart single by Anderson's mother  Liz Anderson  It Do n't Do No Good to Be a Good Girl and There's A Party Goin ' On a hit for Jody Miller that was written by  Anderson  's husband Glenn Sutton and Miller's producer Billy Sherrill that Anderson had wanted for a single release of her own .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "Liz Anderson", "tail": "Anderson"}]


Instruction:
Text: The 2001  Indiana Hoosiers  football team represented  Indiana University  Bloomington during the 2001 NCAA Division I-A football season .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "Indiana Hoosiers", "tail": "Indiana University"}]


Instruction:
Text: Owned by Forum Communications of Fargo which also owns the  Grand Forks Herald   WDAZ  has facilities on South Washington Street in Grand Forks near K-Mart and a news bureau and sales office on U.S. Highway 2 in Devils Lake .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "subsidiaries", "head": "Grand Forks Herald", "tail": "WDAZ"}]


Instruction:
Text: Among them are a 14th century Quran with beautiful calligraphy in Kufic script writings of Avicenna ( Ibn Sina )  Ibn Rushd  (  Averroes  ) El Khwarizmi a translation of Pythagoras treatises on theology astronomy geography and pharmacology .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "Ibn Rushd", "tail": "Averroes"}]


Instruction:
Text: In 2007 the Houston Chronicle listed the album as # 6 on its list of 75 essential Texas blues albums indicating that If  Gilmer  's King had only recorded the song Hide Away  his  legend as an influential blues guitar player would be secure .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "Gilmer", "tail": "his"}]


Instruction:
Text: Finnish American culture is also celebrated at  Finlandia University  in Hancock  Michigan  formerly Suomi College which has been the only Finnish American institution of higher learning in the United States since the closing of Work People's College in Duluth Minnesota in 1941 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "Finlandia University", "tail": "Michigan"}]


Instruction:
Text: The  university  was founded in Newberg Oregon in 1885 by Quaker pioneers originally serving as  Friends Pacific Academy  for several years before becoming a college in 1891 as Pacific College .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "university", "tail": "Friends Pacific Academy"}]


Instruction:
Text: Some staff at Osaka University are represented by the General Union a member of the  National Union of General Workers  ( NUGW ) which is itself a member of the National Trade Union Council (  Zenrokyo  ) .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "members", "head": "National Union of General Workers", "tail": "Zenrokyo"}]


Instruction:
Text: It has hosted the weddings of Bruce Willis and Emma Heming in 2009 and  Ben Affleck  and  Jennifer Garner  in 2005 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "spouse", "head": "Ben Affleck", "tail": "Jennifer Garner"}]


Instruction:
Text: left | thumb | William Hooper Councill ( center ) lawyer editor and founder of Huntsville  Normal  School ( later  Alabama University  ) posing with some of his students.Alabama was originally established by an act of the Alabama State Legislature in 1873 as the State Normal School and University for the Education of the Colored Teachers and Students .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "Normal", "tail": "Alabama University"}]


Instruction:
Text: Martin attended  Denison University  http://www.denison.edu/ in Granville  Ohio  before graduating from Drew University http://www.drew.edu in Madison New Jersey .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "Denison University", "tail": "Ohio"}]


Instruction:
Text: The 2009  Army Black Knights  football team represented the  United States Military Academy  ( USMA or West Point ) in the National Collegiate Athletic Association ( NCAA ) Division I Football Bowl Subdivision ( FBS ) college football during the 2009 season .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "members", "head": "Army Black Knights", "tail": "United States Military Academy"}]


Instruction:
Text: (  Chrysalis  1051 )  Ten Years After  broke up in 1976 and Churchill became Professional Manager at Chrysalis Music ; the company was then owned by his manager Chris Wright .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "subsidiaries", "head": "Chrysalis", "tail": "Ten Years After"}]


Instruction:
Text: In 1999  LucasVarity  was acquired by  TRW  for $ 6.6 bn which sold the Lucas Aerospace section of the company to Goodrich Corporation for $ 1.5 bn in 2002 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "subsidiaries", "head": "LucasVarity", "tail": "TRW"}]


Instruction:
Text: The whole album was produced by Kno and features guest appearances by Thee Tom Hardy Tunji Nemo  Substantial  Sheisty Khrist CunninLynguists members Natti and Deacon the Villain and  QN5 Music  founder Tonedeff .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: James K. Polk  's tomb was located on the front lawn until 1893 when it was moved to the  Tennessee  State Capitol .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: The Science Academy of South Texas  is part of the  South Texas Independent School District  .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "members", "head": "The Science Academy of South Texas", "tail": "South Texas Independent School District"}]


Instruction:
Text: It was the last  Merzbow  release through Masami Akita's label  ZSF Produkt  .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "subsidiaries", "head": "Merzbow", "tail": "ZSF Produkt"}]


Instruction:
Text: Deciding to focus on the Columbia models in March 2003 Neibauer sold the kit side of the company to  Joseph Bartels  a Louisiana attorney and  Lancair  IV-P builder and owner .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: The castle was ransacked and burnt in  1558  by Thomas Radclyffe 3rd Earl of Sussex Lord Deputy of Ireland under orders of Queen  Mary I of England  in retaliation of James's involvement in Ireland against the English .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: In May of that year it was announced that  PLC  Croydon was to remain Presbyterian and PLC Pymble would be transferred with its name changed to  Pymble  Ladies ' College .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "PLC", "tail": "Pymble"}]


Instruction:
Text: Mr Topp came to Bromsgrove with extensive senior management experience from  England  and  Thailand  in both day and boarding schools .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: Two Malaysian astronaut candidates will undergo training at the United States  National Aeronautics  and Space Administration (  NASA  ) this June a senior official said Saturday .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "National Aeronautics", "tail": "NASA"}]


Instruction:
Text: During the 8 month course of production Sebastian travelled to the  United States  and Europe to work with some of the world's best songwriters including his own idol  Brian McKnight  with whom he penned the song ' Wait ' as well as Robin Thicke whom he co-wrote the # 1 charting first single Out With My Baby and Fiend for You .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "countries of residence", "head": "United States", "tail": "Brian McKnight"}]


Instruction:
Text: When the  schools  were consolidated in  1974  Haubstadt student athletes had to participate in the Pocket Athletic Conference .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: The Band appeared at Bob Dylan's 30th anniversary concert celebration in  New York City  in October 1992 where they performed their version of  Dylan  's When I Paint My Masterpiece .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: By  1995  the merger of TSB Group Plc. and Lloyds Bank led Hill Samuel to become a subsidiary of  Lloyds TSB  .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: Viacom  the owner of Paramount Pictures and cable TV channels that include  Comedy Central  sued YouTube in 2007 seeking more than $ 1 billion in damages .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "subsidiaries", "head": "Viacom", "tail": "Comedy Central"}]


Instruction:
Text: Jonny Walker ( born in Preston Lancashire ) is an English rugby league footballer playing for the  Wigan Warriors  in the European Super League competition Blackpool Panthers and  Batley Bulldogs  as a .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: China  's ex-president  Jiang Zemin  takes stage at National Theatre .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "countries of residence", "head": "China", "tail": "Jiang Zemin"}]


Instruction:
Text: Following a final EP for  Touch  and Go 1989's Widowermaker the band left  their  longtime recording partners to sign with longtime supporter Terry Tolkin at Rough Trade Records who had also brought them to Touch and Go for a reportedly generous one-album deal .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: The  2nd Tank Battalion  ( 2nd Tanks ) is an armored battalion of the  United States  Marine Corps which is based out of the Marine Corps Base Camp Lejeune North Carolina .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "members", "head": "2nd Tank Battalion", "tail": "United States"}]


Instruction:
Text: In Eckington was also the Eckington Secondary Modern School on School Street ; when the grammar school became  Derbyshire  's first comprehensive school - the Westfield School - in 1957 this became  Eckington Junior School  .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "Derbyshire", "tail": "Eckington Junior School"}]


Instruction:
Text: Three news programmes a day are broadcast domestically on  TVE1  and internationally on  TVE  Internacional .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "alternate names", "head": "TVE1", "tail": "TVE"}]


Instruction:
Text: Campus facilities Total area Taman Bendahara - m2 Hostel - m2 This campus consists of :  Universiti Malaysia Kelantan  or UMK is a university in the state of  Kelantan  and is the country 19th state university .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "Universiti Malaysia Kelantan", "tail": "Kelantan"}]


Instruction:
Text: Konietzko  stated in 2003 that there was material for what he called MDFMK # 2 but that  Universal Records  was sitting on it .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: NBC Universal  and Microsoft the parents of  msnbc.com  are holding high-level talks about changing its name an unusual and potentially risky endeavor for the third most popular news website in the United States .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "subsidiaries", "head": "NBC Universal", "tail": "msnbc.com"}]


Instruction:
Text: After local newspapers reported that funds from  Arizona  State's foundation were going to support undocumented students and that public funds may have been involved in administering the aid Mr.  Martin  says his office was flooded with hundreds of calls including about a dozen from donors to the university who were very upset .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: The  Spartans  played their home games at Spartan Stadium in East Lansing  Michigan  .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or provinces of residence", "head": "Spartans", "tail": "Michigan"}]


Instruction:
Text: He received an athletic scholarship to attend the University of Florida in Gainesville  Florida  where he played for the  Florida Gators  men's golf team in NCAA competition from 1982 to 1985 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "state or province of headquarters", "head": "Florida", "tail": "Florida Gators"}]


Instruction:
Text: At various points in his career he has held senior editorial posts at The Statesman The Daily Telegraph The  Times  of  India  The Indian Express and most recently India Today where he was Managing Editor till 2003 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "subsidiaries", "head": "Times", "tail": "India"}]


Instruction:
Text: Sonny and Cher had also released three albums to Gold status in the  United States  those being : Look At Us in 1965  Sonny Cher  Live and All I Ever Need Is You both in 1971 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: On December 18 2001 DEVCO sold all surface assets including the international shipping piers railway track railway rights-of-way locomotives and rolling stock and a coal storage facility and locomotive shops at Victoria Junction to 510845 New Brunswick Incorporated a wholly owned subsidiary of  Emera  the holding company which owns  Nova Scotia  Power and operator of the Lingan Generating Station .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "subsidiaries", "head": "Emera", "tail": "Nova Scotia"}]


Instruction:
Text: Ph?m  ;n ( born February 14 1947 ) is a retir  ed Vietn  am Air Force aviator .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "employee of", "head": "Ph?m", "tail": "ed Vietn"}]


Instruction:
Text: In late 1969  Fitzgerald  signed to  Reprise  Records and recorded two albums for the label before returning to a Norman Granz owned label in 1972 when Granz began the Pablo Records label .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "employee of", "head": "Fitzgerald", "tail": "Reprise"}]


Instruction:
Text: eh The David Mamet ( sweet ) of cyberspace remember?eh I carved out my own unique niche of words!eh - D NYC On November 1 1683 the British formed Kings ( aka Brooklyn ) Queens Counties naming the regions in honor of England's King  Charles  II and his wife  Catherine of Braganza  - LITTLE BIG BOOK OF NEW YORK .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "spouse", "head": "Charles", "tail": "Catherine of Braganza"}]


Instruction:
Text: Following this action the congregation of Auburn Avenue Presbyterian Church voted without dissent to withdraw from the  PCA  on January 27 2008 and subsequently joined the Confederation of Reformed  Evangelical  Churches .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: After Johanns resigned on September 20 2007 Conner was named acting secretary.http : / / ap.google.com/article/ALeqM5ie6Sjoie_5El2E5qMem83t7enNAA He served as Acting Secretary of  Agriculture  until President Bush's nominee former North Dakota Governor  Ed Schafer  was confirmed by the U.S. Senate on January 28 2008 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: The  Friedrich-Ebert-Gymnasium  ( FEG ) is a German high school ( see Gymnasium ) in the  Harburg  borough of Hamburg Germany that is known to exist since 1628 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:


Instruction:
Text: He led the University of  North Dakota Fighting Sioux  hockey men's team to Frozen Four NCAA  Division I  championships in 1997 and 2000 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "members", "head": "North Dakota Fighting Sioux", "tail": "Division I"}]


Instruction:
Text: Shahzada  Masoud president of the  Afghanistan  Cricket Federation ( ACF ) warns that his side should not be underestimated and likes to recall a tale of when the national side toured England .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "employee of", "head": "Shahzada", "tail": "Afghanistan"}]


Instruction:
Text: Anneke van Giersbergen  ( formerly Agua de Annique and Anneke van Giersbergen and Agua de Annique ) is the solo project of the former  The Gathering  vocalist Anneke van Giersbergen .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['cities of residence', 'top members employees', 'members', 'alternate names', 'origin', 'state or provinces of residence', 'title of person', 'spouse', 'city of headquarters', 'NA', 'founded by', 'state or province of headquarters', 'employee of', 'countries of residence', 'country of birth', 'founded', 'subsidiaries', 'country of headquarters']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "employee of", "head": "Anneke van Giersbergen", "tail": "The Gathering"}]