Triple

T10159492
Position Surface form Disambiguated ID Type / Status
Subject Kurt Vonnegut E233850 entity
Predicate hasChild P369 FINISHED
Object Nanette Vonnegut E846657 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: Nanette Vonnegut | Statement: [Kurt Vonnegut, hasChild, Nanette Vonnegut]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nanette Vonnegut
Context triple: [Kurt Vonnegut, hasChild, Nanette Vonnegut]
  • A. Edith Vonnegut chosen
    Edith Vonnegut is an American painter and illustrator known for her whimsical, often satirical works that blend everyday life with fantastical elements.
  • B. Mark Vonnegut
    Mark Vonnegut is an American pediatrician and memoirist known for writing about his experiences with mental illness and for being the son of author Kurt Vonnegut.
  • C. Claudia Salinger
    Claudia Salinger is a musically gifted, emotionally sensitive younger sister character from the 1990s television drama "Party of Five."
  • D. Valerie Kafka
    Valerie Kafka was one of Franz Kafka’s sisters and a member of the Kafka family in early 20th-century Prague.
  • E. Nora Zuckerman
    Nora Zuckerman is an American television writer and producer known for her work on genre and mystery series, including serving as an executive producer on the show "Poker Face."
  • 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_69ca848e80748190b91d1e04d35512c7 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cdec56d944819081bc6ea36c905ba2 completed April 2, 2026, 4:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69d32ac043f08190ba7f526d226d3c8c completed April 6, 2026, 3:38 a.m.
Created at: March 30, 2026, 9:09 p.m.