[  [    {      "id": 1,      "text": "Brandon",      "lemma": "Brandon",      "upos": "PROPN",      "xpos": "NNP",      "feats": "Number=Sing",      "head": 2,      "deprel": "nsubj",      "start_char": 0,      "end_char": 7,      "ner": "S-PERSON",      "multi_ner": [        "S-PERSON"      ]    },    {      "id": 2,      "text": "loves",      "lemma": "love",      "upos": "VERB",      "xpos": "VBZ",      "feats": "Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin",      "head": 0,      "deprel": "root",      "start_char": 8,      "end_char": 13,      "ner": "O",      "multi_ner": [        "O"      ]    },    {      "id": 3,      "text": "coffee",      "lemma": "coffee",      "upos": "NOUN",      "xpos": "NN",      "feats": "Number=Sing",      "head": 2,      "deprel": "obj",      "start_char": 14,      "end_char": 20,      "ner": "O",      "multi_ner": [        "O"      ],      "misc": "SpaceAfter=No"    }  ]]

# distance is set to 1 by defaultdef fetch_neigbouring_concepts(concepts, distance):    concepts = sorted(concepts)    for i in range(len(concepts)):        concepts[i].related_concepts = []        for j in range(-distance, distance + 1, 1): # If distance from current concept is less than parameter distance            if i + j >= 0 and i + j < len(concepts): # If index is in bounds
                if j == 0:                    continue
                if concepts[i].name < concepts[i+j].name: # Ensure that we only create one connection between nodes in Neo4J graph                    concepts[i].related_concepts.append(concepts[i+j])

MERGE (c00:Concept {name: 'brandon'})MERGE (c01:Concept {name: 'coffe'})WITH c00, c01MERGE (c00)-[rc00c01:RELATED]->(c01)WITH c00, c01, rc00c01, COALESCE(rc00c01.strength, 0) + 1 AS rc00c01icSET c00.t_index = 1, c01.t_index = 1SET rc00c01.strength = rc00c01icSET rc00c01.t_index = 1

MATCH (n:Concept)WHERE n.name IN ['brandon', 'coffe']RETURN n.name, n.context, n.revision_count

MATCH (n:Concept)WHERE n.name IN ['brandon', 'coffe']WITH n,        CASE n.name                WHEN 'brandon' THEN '. Brandon loves coffee'                WHEN 'coffe' THEN '. Brandon loves coffee'                ELSE n.context        END AS newContext,        CASE n.name                WHEN 'brandon' THEN 0                WHEN 'coffe' THEN 0                ELSE 0        END AS revisionCountSET n.context = newContextSET n.revision_count = revisionCount

MERGE (c00:Concept {name: 'brandon'})MERGE (c01:Concept {name: 'pari'})WITH c00, c01MERGE (c00)-[rc00c01:RELATED]->(c01)WITH c00, c01, rc00c01, COALESCE(rc00c01.strength, 0) + 1 AS rc00c01icSET c00.t_index = 2, c01.t_index = 2SET rc00c01.strength = rc00c01icSET rc00c01.t_index = 2

{    "brandon": {        "context": ". Brandon loves coffee",        "revision_count": 0    },    "pari": {        "context": null,        "revision_count": null    }}

MATCH (n:Concept)WHERE n.name IN ['brandon', 'pari']WITH n,        CASE n.name                WHEN 'brandon' THEN '. Brandon loves coffee. Brandon wants to travel to Paris'                WHEN 'pari' THEN '. Brandon wants to travel to Paris'                ELSE n.context        END AS newContext,        CASE n.name                WHEN 'brandon' THEN 1                WHEN 'pari' THEN 0                ELSE 0        END AS revisionCountSET n.context = newContextSET n.revision_count = revisionCount

MATCH (startNode:Concept{name: 'pari'})CALL apoc.path.spanningTree(startNode, {relationshipFilter: "", minLevel: 0, maxLevel: 2}) YIELD pathWITH path, nodes(path) as pathNodes, startNode.t_index as current_tUNWIND range(0, size(pathNodes)-1) AS indexWITH path, pathNodes[index] as node, current_tORDER BY node.t_index DESCWHERE node.t_index <= current_t AND node.t_index >= current_t - 15WITH DISTINCT node LIMIT 800MATCH ()-[relation]->()RETURN node, relation

{    "pari": ". Brandon wants to travel to Paris",    "brandon": ". Brandon loves coffee. Brandon wants to travel to Paris",    "coffe": ". Brandon loves coffee"}

 graph_concepts = graph_concept_nodes.values()    for concept in graph_concepts:        concept.sort_val = 0        for relation in concept.related_concepts:            concept.sort_val += (relation.t_index * 3) + relation.strength     # TODO: 3 is a hyperparameter, move this to header    graph_concepts = sorted(graph_concepts, key=lambda c: c.sort_val)    graph_concepts.reverse()    # We want them in descending order, so that highest temporal relations are first

. Brandon loves coffee # from related concept: coffee
. Brandon loves coffee. Brandon wants to travel to Paris # from related concept: brandon
. Brandon wants to travel to Paris # from essential concepts

Using the following statements when necessary, answer the question that follows. Each sentence in the following statements is true when read in chronological order: 
statements:. Brandon loves coffee
. Brandon loves coffee. Brandon wants to travel to Paris
. Brandon wants to travel to Paris
question:Who wants to travel to Paris?
Answer: