Is there an efficient way to map the reconciled CSV to the unreconciled RDF?

[Yueqiao12Zhang]

Example JSON to RDF in one record
Note: all predicate maps are made-up

{
    "genres": [],
    "video": false,
    "title": "Suck",
    "artist-credit": [
        {
            "artist": {
                "type-id": "e431f5f6-b5d2-343d-8b36-72607fffb74b",
                "aliases": [],
                "name": "Pigface",
                "sort-name": "Pigface",
                "genres": [
                    {
                        "name": "electronic",
                        "count": 1,
                        "disambiguation": "",
                        "id": "89255676-1f14-4dd8-bbad-fca839d6aff4"
                    },
                    {
                        "disambiguation": "",
                        "count": 4,
                        "id": "ffbc9907-c9be-4ace-876b-b7fd5b9d51f9",
                        "name": "industrial rock"
                    }
                ],
                "tags": [
                    {
                        "count": 1,
                        "name": "american"
                    },
                    {
                        "count": 1,
                        "name": "electronic"
                    },
                    {
                        "name": "industrial rock",
                        "count": 4
                    },
                    {
                        "name": "supergroup",
                        "count": 2
                    }
                ],
                "id": "11137c88-a9a2-4ffa-a97d-fb058c6d6ce2",
                "disambiguation": "industrial supergroup",
                "type": "Group"
            },
            "joinphrase": "",
            "name": "Pigface"
        }
    ],
    "rating": {
        "value": null,
        "votes-count": 0
    },
    "disambiguation": "",
    "annotation": null,
    "aliases": [],
    "length": null,
    "tags": [],
    "relations": [
        {
            "end": null,
            "attributes": [],
            "attribute-values": {},
            "attribute-ids": {},
            "work": {
                "attributes": [],
                "title": "Suck",
                "type": "Song",
                "disambiguation": "",
                "id": "20309de2-2847-3753-8a7a-d0e335f57176",
                "type-id": "f061270a-2fd6-32f1-a641-f0f8676d14e6",
                "language": "eng",
                "iswcs": [
                    "T-070.172.108-5",
                    "T-900.245.777-4",
                    "T-905.188.140-4",
                    "T-905.318.598-1",
                    "T-926.281.906-7"
                ],
                "languages": [
                    "eng"
                ]
            },
            "direction": "forward",
            "type-id": "a3005666-a872-32c3-ad06-98af558e99b0",
            "target-type": "work",
            "ended": false,
            "target-credit": "",
            "source-credit": "",
            "type": "performance",
            "begin": null
        }
    ],
    "id": "10b9c34b-821d-4cc8-9587-5c7d0cb68865",
}

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix wdt: <http://www.wikidata.org/prop/direct/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<10b9c34b-821d-4cc8-9587-5c7d0cb68865> wdt:P17 [ wdt:P87 "Pigface" ;
            wdt:P96 [ wdt:P106 "e431f5f6-b5d2-343d-8b36-72607fffb74b" ;
                    wdt:P119 "11137c88-a9a2-4ffa-a97d-fb058c6d6ce2" ;
                    wdt:P45 [ wdt:P49 2 ;
                            wdt:P87 "supergroup" ],
                        [ wdt:P49 4 ;
                            wdt:P87 "industrial rock" ],
                        [ wdt:P49 1 ;
                            wdt:P87 "electronic" ],
                        [ wdt:P49 1 ;
                            wdt:P87 "american" ] ;
                    wdt:P57 "Pigface" ;
                    wdt:P72 "industrial supergroup" ;
                    wdt:P79 "Group" ;
                    wdt:P87 "Pigface" ;
                    wdt:P97 [ wdt:P119 "89255676-1f14-4dd8-bbad-fca839d6aff4" ;
                            wdt:P49 1 ;
                            wdt:P87 "electronic" ],
                        [ wdt:P119 "ffbc9907-c9be-4ace-876b-b7fd5b9d51f9" ;
                            wdt:P49 4 ;
                            wdt:P87 "industrial rock" ] ] ] ;
    wdt:P22 "Suck" ;
    wdt:P23 [ wdt:P106 "a3005666-a872-32c3-ad06-98af558e99b0" ;
            wdt:P11 [ ] ;
            wdt:P14 "work" ;
            wdt:P51 "forward" ;
            wdt:P6 [ ] ;
            wdt:P79 "performance" ;
            wdt:P84 [ wdt:P106 "f061270a-2fd6-32f1-a641-f0f8676d14e6" ;
                    wdt:P111 "eng" ;
                    wdt:P119 "20309de2-2847-3753-8a7a-d0e335f57176" ;
                    wdt:P13 [ rdf:value "T-900.245.777-4" ],
                        [ rdf:value "T-070.172.108-5" ],
                        [ rdf:value "T-926.281.906-7" ],
                        [ rdf:value "T-905.318.598-1" ],
                        [ rdf:value "T-905.188.140-4" ] ;
                    wdt:P22 "Suck" ;
                    wdt:P68 [ rdf:value "eng" ] ;
                    wdt:P79 "Song" ] ] .

[Yueqiao12Zhang]

Current idea: import to OpenRefine, delete all data except the ones that I want to reconcile, merge them to one or a few columns, reconcile, then map them back to RDF.