Triple

T13921261
Position Surface form Disambiguated ID Type / Status
Subject Barry Weisselberg E334748 entity
Predicate familyName P18 FINISHED
Object Weisselberg E304327 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: Weisselberg | Statement: [Barry Weisselberg, familyName, Weisselberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Weisselberg
Context triple: [Barry Weisselberg, familyName, Weisselberg]
  • A. Weisselberg chosen
    Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
  • B. Wirsberg
    Wirsberg is a small market town in the Upper Franconia region of Bavaria, Germany, known for its scenic location in the Franconian Forest and its historic architecture.
  • C. Wielenbach
    Wielenbach is a small municipality in the Upper Bavarian region of Germany, situated within the district of Weilheim-Schongau.
  • D. Voitsberg
    Voitsberg is a small town in southeastern Austria known for its industrial heritage and location within the federal state of Styria.
  • E. Irschenberg
    Irschenberg is a Bavarian municipality in southern Germany known for its scenic location in the Alpine foothills and its prominent motorway ascent on the A8 between Munich and Salzburg.
  • 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_69d81c5f739081908bc05b2461f54828 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2aa5c1f481908a9d8786872f08fe completed April 14, 2026, 11:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69fba1c45ba4819096233570d8d4afec completed May 6, 2026, 8:17 p.m.
Created at: April 9, 2026, 10:16 p.m.