Triple

T10615508
Position Surface form Disambiguated ID Type / Status
Subject Harvey Dunn E276107 entity
Predicate hasNotableStudent P4838 FINISHED
Object Harold von Schmidt E562375 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: Harold von Schmidt | Statement: [Harvey Dunn, hasNotableStudent, Harold von Schmidt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Harold von Schmidt
Context triple: [Harvey Dunn, hasNotableStudent, Harold von Schmidt]
  • A. Harold von Schmidt chosen
    Harold von Schmidt was an American illustrator best known for his evocative magazine and book illustrations, particularly in the Western genre.
  • B. Robert von Hagge
    Robert von Hagge was a prominent American golf course architect known for designing numerous acclaimed and visually dramatic courses around the world.
  • C. Hans Schmidt
    Hans Schmidt is a common German personal name shared by numerous individuals across fields such as sports, politics, and the arts.
  • D. Erich Schiffmann
    Erich Schiffmann is an American yoga master and author known for his influential teachings on intuitive, meditative yoga practice.
  • E. Hermann Hager
    Hermann Hager was a German pharmacist and pharmaceutical chemist known for his influential reference works and contributions to pharmaceutical practice in the 19th century.
  • 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_69d6aaf948d88190806cc3a8c47a3fb2 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d6df6d76dc8190bd8d481fed3225d9 completed April 8, 2026, 11:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4cbacad608190adddd91f13e4113b completed April 19, 2026, 12:33 p.m.
Created at: April 8, 2026, 7:33 p.m.