Triple

T5642028
Position Surface form Disambiguated ID Type / Status
Subject Anna Faris E124287 entity
Predicate twitterUsername P2943 FINISHED
Object AnnaKFaris E124287 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: AnnaKFaris | Statement: [Anna Faris, twitterUsername, AnnaKFaris]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AnnaKFaris
Context triple: [Anna Faris, twitterUsername, AnnaKFaris]
  • A. Anna Faris chosen
    Anna Faris is an American actress and comedian best known for her lead role in the Scary Movie film series and her work in both film and television comedy.
  • B. Amy Ferson
    Amy Ferson is an American journalist and commentator best known as the first wife of television personality and news anchor T. J. Holmes.
  • C. Julianna Farrait
    Julianna Farrait is best known as the wife of notorious Harlem drug trafficker Frank Lucas and for her involvement in his high-profile criminal lifestyle.
  • D. Adrienne Fazan
    Adrienne Fazan was an American film editor best known for her long collaboration with MGM and director Vincente Minnelli, including work on classic Hollywood musicals.
  • E. Marilu Henner
    Marilu Henner is an American actress and author best known for her role as Elaine Nardo on the TV sitcom "Taxi" and for her appearances in numerous film and television projects.
  • 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_69c00824643c81909ffdb888a2d35189 completed March 22, 2026, 3:17 p.m.
NER Named-entity recognition batch_69c022a6a22881908d16f4df564ed2a2 completed March 22, 2026, 5:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69c04d7c98008190b79528596eca4208 completed March 22, 2026, 8:13 p.m.
Created at: March 22, 2026, 3:41 p.m.