Triple

T6788808
Position Surface form Disambiguated ID Type / Status
Subject Funny You Should Ask E155879 entity
Predicate notableComediansAppearing P10205 FINISHED
Object Tiffany Haddish E332986 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: Tiffany Haddish | Statement: [Funny You Should Ask, notableComediansAppearing, Tiffany Haddish]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tiffany Haddish
Context triple: [Funny You Should Ask, notableComediansAppearing, Tiffany Haddish]
  • A. Tiffany Haddish chosen
    Tiffany Haddish is an American stand-up comedian and actress known for her breakout role in "Girls Trip" and her energetic, unfiltered comedic style.
  • B. Regina Hall
    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.
  • C. Leslie Jones
    Leslie Jones is an American film editor known for her work on major Hollywood productions, including the feature film "Starsky & Hutch."
  • D. Issa Rae
    Issa Rae is an American actress, writer, and producer best known for creating and starring in the HBO series "Insecure."
  • E. Melissa McCarthy
    Melissa McCarthy is an American actress and comedian known for her breakout comedic role in "Bridesmaids" and subsequent work in film and television.
  • 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_69c6881770fc8190972b2906390380f5 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d7ca96008190ba79563c2a9a9b0e completed March 27, 2026, 7:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69c71a8998408190b741417ce6f21f55 completed March 28, 2026, 12:02 a.m.
Created at: March 27, 2026, 2:14 p.m.