Triple

T18768206
Position Surface form Disambiguated ID Type / Status
Subject Sibert E458944 entity
Predicate spellingVariantOf P457 FINISHED
Object Seibert (possible variant) 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: Seibert (possible variant) | Statement: [Sibert, spellingVariantOf, Seibert (possible variant)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Seibert (possible variant)
Context triple: [Sibert, spellingVariantOf, Seibert (possible variant)]
  • A. Seibert chosen
    Seibert is a German-origin surname borne by various notable individuals across fields such as sports, science, and the arts.
  • B. Siebert
    Siebert is a surname most notably associated with Sonny Siebert, an American Major League Baseball pitcher active in the 1960s and 1970s.
  • C. Selbitz
    Selbitz is a small town in the Upper Franconia region of Bavaria, Germany, known for its picturesque setting near the Franconian Forest.
  • D. Sieber
    Sieber is a small river in the German state of Lower Saxony that flows through the Harz Mountains and into the Oder.
  • E. Sieber
    Sieber is the family name of Maria Riva, the actress and author who was the daughter of film legend Marlene Dietrich.
  • 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_69d8d395dba0819087568404508590cb completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e58d867264819098ae35c9feab3bb4 completed April 20, 2026, 2:20 a.m.
Created at: April 10, 2026, 11:52 a.m.