code2

import spacy
nlp = spacy.load("en_core_web_sm")


def extract_claim_triples(text):
    doc = nlp(text)
    claim_triples = []

    for sent in doc.sents:
        subject = None
        verb = None
        obj = None

        for token in sent:
            if token.dep_ == "ROOT" and token.pos_ == "VERB":
                verb = token.text
                for child in token.children:
                    if child.dep_ in {"nsubj", "nsubjpass"}:
                        subject = child.text
                        break
                for child in token.children:
                    if child.dep_ in {"dobj", "attr", "acomp"}:
                        obj = child.text
                        break

        if not subject:
            for token in sent:
                if token.dep_ in {"nsubj", "nsubjpass"}:
                    subject = token.text
                    break
        if not obj:
            for token in sent:
                if token.dep_ in {"dobj", "attr", "acomp"}:
                    obj = token.text
                    break

        if subject and verb and obj:
            claim_triples.append((subject, verb, obj))

    return claim_triples


if __name__ == "__main__":
    sample_text = (
        "The patient exhibits fever. "
        "A high temperature indicates an infection. "
        "The patient also reports a severe cough, which may be a sign of pneumonia."
    )
    claim_triples = extract_claim_triples(sample_text)
    print(claim_triples)