Triple

T15998444
Position Surface form Disambiguated ID Type / Status
Subject Marsai Martin E388034 entity
Predicate workedWith P398 FINISHED
Object Regina Hall E65704 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: Regina Hall | Statement: [Marsai Martin, workedWith, Regina Hall]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Regina Hall
Context triple: [Marsai Martin, workedWith, Regina Hall]
  • A. Regina Hall chosen
    Regina Hall is an American actress and comedian known for her roles in films such as the Scary Movie series, Girls Trip, and numerous television comedies.
  • B. Leslie Jones
    Leslie Jones is an American film editor known for her work on major Hollywood productions, including the feature film "Starsky & Hutch."
  • C. Leslie Jones
    Leslie Jones is an American comedian and actress known for her work on "Saturday Night Live" and roles in films such as the 2016 "Ghostbusters" reboot.
  • D. Tiffany Haddish
    Tiffany Haddish is an American stand-up comedian and actress known for her breakout role in "Girls Trip" and her energetic, unfiltered comedic style.
  • E. Da'Vine Joy Randolph
    Da'Vine Joy Randolph is an American actress and singer known for her acclaimed performances in film, television, and theater, including her award-winning role in "The Holdovers."
  • 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_69d86daa562c81908aacc179c0fe8fb5 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e157893ebc8190acb75ee05e450fae completed April 16, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffcf1edc7c81908fdd0fa00418d7a7 completed May 10, 2026, 12:19 a.m.
Created at: April 10, 2026, 4:55 a.m.