Triple

T6457127
Position Surface form Disambiguated ID Type / Status
Subject Darren Star E142020 entity
Predicate creatorOf P806 FINISHED
Object Miss Match E594866 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: Miss Match | Statement: [Darren Star, creatorOf, Miss Match]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Miss Match
Context triple: [Darren Star, creatorOf, Miss Match]
  • A. Miss Match chosen
    Miss Match is an early-2000s American romantic comedy-drama television series about a high-powered divorce lawyer who moonlights as a matchmaker.
  • B. The Perfect Match
    The Perfect Match is a romantic comedy film produced by Flavor Unit Entertainment that follows a commitment-phobic bachelor whose views on love are challenged by an unexpected relationship.
  • C. Miss Melody
    "Miss Melody" is a hip-hop-influenced violin track by The Hip-Hop Violinist that blends classical string performance with contemporary urban beats.
  • D. Miss
    Miss is a traditional English honorific used before the surname or full name of an unmarried or younger woman.
  • E. Miss Bianca
    Miss Bianca is a sophisticated and brave white mouse who serves as one of the heroic rescuers in Disney’s animated adventure "The Rescuers."
  • 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_69c008d2f91c8190a8178767a35e08fc completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c069d639ec8190bb0a806da4118440 completed March 22, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69c65394d8b481909868faf79f3d2383 completed March 27, 2026, 9:53 a.m.
Created at: March 22, 2026, 4:48 p.m.