Triple

T17514491
Position Surface form Disambiguated ID Type / Status
Subject Sex Therapy E426532 entity
Predicate hasPart P35 FINISHED
Object Mr. Right NE NERFINISHED

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: Mr. Right | Statement: [Sex Therapy, hasPart, Mr. Right]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mr. Right
Context triple: [Sex Therapy, hasPart, Mr. Right]
  • A. Mr. Right chosen
    Mr. Right is a 2015 action-romantic comedy film in which Sam Rockwell plays a eccentric hitman who falls in love while being pursued by his former employers.
  • B. Making Mr. Right
    Making Mr. Right is a 1987 romantic science-fiction comedy film about a publicist tasked with humanizing an emotionless android, starring Ann Magnuson and John Malkovich.
  • C. Mr. Right Now
    "Mr. Right Now" is a hip-hop track by 21 Savage and Metro Boomin featuring Drake, known for its smooth production and themes of casual romance and fleeting relationships.
  • D. The Right Man
    "The Right Man" is a song by American singer Christina Aguilera, known as an emotional ballad reflecting on love and commitment.
  • E. Mr. Wonderful
    Mr. Wonderful is a 1993 romantic comedy film about a New York electrician who schemes to find a new husband for his ex-wife so he can afford his dream business.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889dd9164819087b1dc3c9240c870 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e4525fa0c48190b42b36c40db7ed7f completed April 19, 2026, 3:56 a.m.
Created at: April 10, 2026, 5:49 a.m.