Triple

T17876072
Position Surface form Disambiguated ID Type / Status
Subject Siegl E446956 entity
Predicate hasVariant P455 FINISHED
Object Siegel 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: Siegel | Statement: [Siegl, hasVariant, Siegel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siegel
Context triple: [Siegl, hasVariant, Siegel]
  • A. Siegel chosen
    Siegel is the surname of Benjamin "Bugsy" Siegel, the infamous American mobster who played a key role in the development of Las Vegas.
  • B. Sigel
    Sigel is a surname most notably associated with American cinematographer Newton Thomas Sigel, known for his work on major Hollywood films.
  • C. Siegert
    Siegert is a German-language surname most notably associated with Johann Gottlieb Benjamin Siegert, the 19th-century creator of Angostura bitters.
  • D. Seidenberg
    Seidenberg is a German surname most notably associated with former professional ice hockey defenseman Dennis Seidenberg.
  • E. Schechter
    Schechter is a Jewish surname most notably associated with Solomon Schechter, a prominent rabbi and scholar who helped shape Conservative Judaism.
  • 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_69d8b9f4c22c819093c2680434472894 completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e49aa614b48190bdc9e905e9e6d5e0 completed April 19, 2026, 9:04 a.m.
Created at: April 10, 2026, 10:18 a.m.