Triple

T12327060
Position Surface form Disambiguated ID Type / Status
Subject Dan Futterman E293857 entity
Predicate spouse P13 FINISHED
Object Anya Epstein E567904 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: Anya Epstein | Statement: [Dan Futterman, spouse, Anya Epstein]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Anya Epstein
Context triple: [Dan Futterman, spouse, Anya Epstein]
  • A. Anya Epstein chosen
    Anya Epstein is an American television writer and producer known for her work on acclaimed drama series such as The Affair.
  • B. Alexandra Papenfus
    Alexandra Papenfus is a person after whom another individual named Alexandra was named, suggesting she holds personal or familial significance to the namer.
  • C. Alina Margolis
    Alina Margolis was a Polish Jewish pediatrician, Holocaust survivor, and humanitarian activist known for her medical and social work, including with organizations aiding Jews during and after World War II.
  • D. Danielle Judovits
    Danielle Judovits is an American voice actress known for her work in animated television series and video games.
  • E. Alisa Lepselter
    Alisa Lepselter is an American film editor best known for her long-time collaboration with director Woody Allen on numerous critically acclaimed films.
  • 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_69d6ab6ae0dc8190b1522a9c1c55c114 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f4f90a881908c5060dd197744d1 completed April 10, 2026, 6:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f64b8ed1dc81908a0066d7cbfda086 completed May 2, 2026, 7:07 p.m.
Created at: April 8, 2026, 9:53 p.m.