@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix orcid: <https://orcid.org/> .
@prefix this: <http://purl.org/np/RA7jfuFKxkUh8olqadPGl36eb1XtpnvbVD6u1gIlCWouM> .
@prefix sub: <http://purl.org/np/RA7jfuFKxkUh8olqadPGl36eb1XtpnvbVD6u1gIlCWouM#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix pav: <http://purl.org/pav/> .
@prefix np: <http://www.nanopub.org/nschema#> .
@prefix doco: <http://purl.org/spar/doco/> .
@prefix c4o: <http://purl.org/spar/c4o/> .

sub:Head {
  this: np:hasAssertion sub:assertion;
    np:hasProvenance sub:provenance;
    np:hasPublicationInfo sub:pubinfo;
    a np:Nanopublication .
}

sub:assertion {
  sub:paragraph c4o:hasContent "While looking at the language-tagged strings in “English” (in RDF @en), Figure 6b shows that the experts perform very well when discerning whether a given value is an English text or not. The crowd was less successful in the following two situations: (i) the value corresponded to a number and the remaining data was specified in English, e.g., (St. Louis School Hong Kong, founded, 1864); and (ii) the value was a text without special characters, but in a different language than English, for example German (Woellersdorf-Stein abrueckl, Art, Marktgemeinde). The performance of both crowdsourcing approaches for the remaining datatypes were similar or not relevant due the low number of triples processed.";
    a doco:Paragraph .
}

sub:provenance {
  sub:assertion prov:hadPrimarySource <http://dx.doi.org/10.3233/SW-160239>;
    prov:wasAttributedTo orcid:0000-0003-0530-4305 .
}

sub:pubinfo {
  this: dcterms:created "2019-09-20T18:05:11+01:00"^^xsd:dateTime;
    pav:createdBy orcid:0000-0002-7114-6459 .
}