Triple

T7230982
Position Surface form Disambiguated ID Type / Status
Subject Love Wins E154899 entity
Predicate subjectPerson P2308 FINISHED
Object John Arthur E170143 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: John Arthur | Statement: [Love Wins, subjectPerson, John Arthur]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Arthur
Context triple: [Love Wins, subjectPerson, John Arthur]
  • A. John Arthur chosen
    John Arthur was the late husband of James Obergefell, whose death and their marriage became central to the landmark U.S. Supreme Court case legalizing same-sex marriage nationwide.
  • B. Charles Lang
    Charles Lang was an acclaimed American cinematographer known for his work on numerous classic Hollywood films across several decades.
  • C. Harold Hartley
    Harold Hartley was a British chemist and academic known for his contributions to physical chemistry and his leadership roles in scientific organizations.
  • D. Edward Arnold
    Edward Arnold was a prominent American character actor of the early to mid-20th century, known for his powerful screen presence and frequent portrayals of authoritative or villainous figures in Hollywood films.
  • E. Edward Arnold
    Edward Arnold was a British publishing house active in the late 19th and 20th centuries, known for issuing literary works and academic texts.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69c68811dd1c8190ac460bb39e64e1f0 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ea0f09648190b285993556f704d5 completed March 27, 2026, 8:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7cc22a39481909a2f38014260f302 completed March 28, 2026, 12:40 p.m.
Created at: March 27, 2026, 2:54 p.m.