Triple

T3769637
Position Surface form Disambiguated ID Type / Status
Subject Walter Eucken E82764 entity
Predicate familyName P18 FINISHED
Object Eucken E358894 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: Eucken | Statement: [Walter Eucken, familyName, Eucken]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eucken
Context triple: [Walter Eucken, familyName, Eucken]
  • A. Rudolf Eucken chosen
    Rudolf Eucken was a German philosopher and Nobel Prize in Literature laureate known for his work on ethical activism and spiritual life.
  • B. Hermann Hess
    Hermann Hess was a mountaineer known for making the first recorded ascent of Monte San Valentín, the highest peak in Chilean Patagonia.
  • C. Paul Körner
    Paul Körner was a high-ranking Nazi official and close associate of Hermann Göring who played a key role in the economic and industrial mobilization of the Third Reich.
  • D. Hermann Kallenbach
    Hermann Kallenbach was a German-Jewish architect and close associate of Mahatma Gandhi who became one of his most devoted supporters during Gandhi’s years in South Africa.
  • E. Paul Junger Witt
    Paul Junger Witt was an American television and film producer best known for creating and producing popular sitcoms such as "The Golden Girls" and "Soap."
  • 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_69ad8b207b0081909d2b48843fbd8795 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcc2f016c81909c2e3c85dbc3c259 completed March 8, 2026, 7:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4e5287908819084319b8dfa407635 completed March 14, 2026, 4:33 a.m.
Created at: March 8, 2026, 3:35 p.m.