Triple

T5582830
Position Surface form Disambiguated ID Type / Status
Subject Tatiana Schlossberg E146679 entity
Predicate sibling P363 FINISHED
Object Rose Schlossberg E235734 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: Rose Schlossberg | Statement: [Tatiana Schlossberg, sibling, Rose Schlossberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rose Schlossberg
Context triple: [Tatiana Schlossberg, sibling, Rose Schlossberg]
  • A. Rose Schlossberg chosen
    Rose Schlossberg is an American actress, comedian, and web series creator, and the eldest grandchild of former U.S. President John F. Kennedy.
  • B. Rachel Leibowitz
    Rachel Leibowitz is a person notable enough to be specifically cited as a bearer of the surname Leibowitz.
  • C. Nancy Lieberman
    Nancy Lieberman is a pioneering American basketball player and coach, widely regarded as one of the greatest figures in women's basketball history and a trailblazer for women in the sport.
  • D. Liz Gorinsky
    Liz Gorinsky is an acclaimed science fiction and fantasy editor known for her influential work at Tor Books and for winning major genre awards.
  • E. June Preisser
    June Preisser was an American film actress and dancer best known for her energetic supporting roles in 1930s and 1940s Hollywood musicals, often playing peppy, acrobatic teenagers.
  • 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_69c0090287a08190b4098411effe970c completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c0208333f08190bf0049b6bdd280f5 completed March 22, 2026, 5:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0bf5e1fcc8190b9ab67a9cc9e1be0 completed March 23, 2026, 4:19 a.m.
Created at: March 22, 2026, 3:37 p.m.