Triple

T12925870
Position Surface form Disambiguated ID Type / Status
Subject Cry Pretty E309242 entity
Predicate single P3283 FINISHED
Object Love Wins E154899 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: Love Wins | Statement: [Cry Pretty, single, Love Wins]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Love Wins
Context triple: [Cry Pretty, single, Love Wins]
  • A. Love Wins chosen
    Love Wins is a nonfiction book by James Obergefell that recounts his personal journey as the lead plaintiff in the landmark U.S. Supreme Court case that legalized same-sex marriage nationwide.
  • B. Loving
    Loving is an American daytime soap opera created by Agnes Nixon that aired on ABC from 1983 to 1995.
  • C. Loving
    Loving is a 2016 historical drama film that portrays the real-life interracial couple Richard and Mildred Loving and their landmark U.S. Supreme Court case that invalidated laws prohibiting interracial marriage.
  • D. Loving
    Loving is a small village located in Eddy County in the southeastern region of the U.S. state of New Mexico.
  • E. Love, American Style
    Love, American Style is an American television anthology comedy series from the late 1960s and 1970s that presented humorous, self-contained stories about romance and relationships.
  • 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_69d7bdfa933c8190b5a27aa4a08a19b7 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d971eb17c88190bf523da897172a0c completed April 10, 2026, 9:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6b8d39d4c81908fab65129f292862 completed May 3, 2026, 2:54 a.m.
Created at: April 9, 2026, 5:42 p.m.