Triple

T15859854
Position Surface form Disambiguated ID Type / Status
Subject Sing Sing E384554 entity
Predicate hasNotableInmate P1092 FINISHED
Object Ruth Snyder NE NERFINISHED

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: Ruth Snyder | Statement: [Sing Sing, hasNotableInmate, Ruth Snyder]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ruth Snyder
Context triple: [Sing Sing, hasNotableInmate, Ruth Snyder]
  • A. Ruth Snyder chosen
    Ruth Snyder was an American woman infamously executed in 1928 for the murder of her husband, a case that became notorious due to a secretly photographed image of her electrocution published in the press.
  • B. Ruth Robbins
    Ruth Robbins is an academic and author known for her work in literary and cultural studies.
  • C. Ruth Terry
    Ruth Terry was an American actress and singer known for her roles in 1930s and 1940s Hollywood films, particularly in musicals and Westerns.
  • D. Ruth Byerly
    Ruth Byerly is known primarily as the first wife of American television executive and producer Grant Tinker.
  • E. Ruth Nelson
    Ruth Nelson was an American stage and film actress known for her work with the Group Theatre and roles in classic mid-20th-century films.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d86da422088190aac39e32e6c68429 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1555a1f008190bb3f03b0f35ed8a4 completed April 16, 2026, 9:32 p.m.
Created at: April 10, 2026, 4:50 a.m.