Triple

T19578968
Position Surface form Disambiguated ID Type / Status
Subject Klaus Schwarzkopf E489933 entity
Predicate hasWikipediaPage P7959 FINISHED
Object dewiki:Klaus_Schwarzkopf 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: dewiki:Klaus_Schwarzkopf | Statement: [Klaus Schwarzkopf, hasWikipediaPage, dewiki:Klaus_Schwarzkopf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: dewiki:Klaus_Schwarzkopf
Context triple: [Klaus Schwarzkopf, hasWikipediaPage, dewiki:Klaus_Schwarzkopf]
  • A. Klaus Schwarzkopf chosen
    Klaus Schwarzkopf was a German actor best known for his roles in film and television during the mid-20th century.
  • B. Hans Schwarzkopf
    Hans Schwarzkopf was a German chemist and entrepreneur best known for founding the Schwarzkopf hair care brand and pioneering modern shampoo products.
  • C. Rudolf Schwarzkopf
    Rudolf Schwarzkopf is a notable individual who carries the German surname Schwarzkopf, associated with various prominent figures in fields such as music, military, and industry.
  • D. Klaus-Georg
    Klaus-Georg is a masculine given name of German origin, typically used as a compound form combining "Klaus" and "Georg."
  • E. Karl-Heinz Kohl
    Karl-Heinz Kohl is a German ethnologist and anthropologist known for his work on religious cultures, particularly in Indonesia and Oceania.
  • 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_69d8e8dd9374819098e36349b3211663 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e6402693d88190a828c0e136895783 completed April 20, 2026, 3:03 p.m.
Created at: April 10, 2026, 1:42 p.m.