Triple

T16810566
Position Surface form Disambiguated ID Type / Status
Subject Mr. Nanny E408596 entity
Predicate starring P1507 FINISHED
Object Sherman Hemsley E164742 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: Sherman Hemsley | Statement: [Mr. Nanny, starring, Sherman Hemsley]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sherman Hemsley
Context triple: [Mr. Nanny, starring, Sherman Hemsley]
  • A. Sherman Hemsley chosen
    Sherman Hemsley was an American actor and comedian best known for his iconic portrayal of George Jefferson on the television sitcoms All in the Family and The Jeffersons.
  • B. Redd Foxx
    Redd Foxx was a pioneering American stand-up comedian and actor known for his raw, boundary-pushing comedy and his iconic role as Fred Sanford on the television sitcom "Sanford and Son."
  • C. Frank R. Gooding
    Frank R. Gooding was an early 20th-century Idaho politician who served as the state's governor and later as a U.S. senator.
  • D. Harvey Korman
    Harvey Korman was an American comedic actor best known for his work on *The Carol Burnett Show* and in Mel Brooks films such as *Blazing Saddles* and *History of the World, Part I*.
  • E. Frankie Faison
    Frankie Faison is an American actor known for his character roles in film and television, including appearances in projects like "The Wire," the "Hannibal Lecter" film series, and numerous comedies and dramas.
  • 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_69d88393905081908d00a86b99996ac8 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3b2cf680c8190bcd640570c524918 completed April 18, 2026, 4:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00b290d8e4819082880444b42ffa43 completed May 10, 2026, 4:30 p.m.
Created at: April 10, 2026, 5:23 a.m.