Triple

T7999568
Position Surface form Disambiguated ID Type / Status
Subject Dietmar Hopp E186211 entity
Predicate coFoundedWith P2835 FINISHED
Object Hasso Plattner E187923 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: Hasso Plattner | Statement: [Dietmar Hopp, coFoundedWith, Hasso Plattner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hasso Plattner
Context triple: [Dietmar Hopp, coFoundedWith, Hasso Plattner]
  • A. Hasso Plattner chosen
    Hasso Plattner is a German billionaire entrepreneur and philanthropist best known as a co-founder of the enterprise software giant SAP and a prominent patron of science and the arts.
  • B. Klaus Tschira
    Klaus Tschira was a German physicist, entrepreneur, and philanthropist best known as one of the co-founders of the software company SAP.
  • C. Dietmar Hopp
    Dietmar Hopp is a German billionaire businessman and philanthropist best known as a co-founder of the software company SAP and a major patron of sports and medical research.
  • D. Gerhard Weikum
    Gerhard Weikum is a prominent German computer scientist known for his influential research in database systems, information retrieval, and knowledge bases.
  • E. Tobias Nipkow
    Tobias Nipkow is a German computer scientist known for his influential work in interactive theorem proving and formal verification, particularly through his contributions to the Isabelle proof assistant.
  • 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_69ca82aaaf24819084b94d18f699ba53 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3c9b67988190a8a4a2c960017400 completed March 31, 2026, 3:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc56910dd4819084ffe3350f15d95d completed March 31, 2026, 11:19 p.m.
Created at: March 30, 2026, 5:17 p.m.