Triple

T2992387
Position Surface form Disambiguated ID Type / Status
Subject Martinair E80785 entity
Predicate hasKeyPerson P256 FINISHED
Object Martin Schröder E320964 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: Martin Schröder | Statement: [Martinair, hasKeyPerson, Martin Schröder]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Martin Schröder
Context triple: [Martinair, hasKeyPerson, Martin Schröder]
  • A. Martin Schröder chosen
    Martin Schröder is a Dutch aviation entrepreneur best known as the founder of the charter airline Martinair.
  • B. Christian Scholz
    Christian Scholz is a German computer scientist and open-source developer known for his contributions to web technologies and social software.
  • C. Peter Kohl
    Peter Kohl is a German businessman and author best known as the son of former German Chancellor Helmut Kohl.
  • D. Andreas Schulze
    Andreas Schulze is a contemporary German painter known for his large-scale, stylized depictions of everyday objects and interiors, often rendered with a playful yet subtly critical approach.
  • E. Johannes Popitz
    Johannes Popitz was a German lawyer, conservative politician, and high-ranking finance official who served as Prussian finance minister and later became involved in resistance circles against the Nazi regime.
  • 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_69ad8b16c3488190b47b6aa7a59a335b completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad99e12c5c8190af7cc20e4c48bf45 completed March 8, 2026, 3:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1eee1433c8190bdee291c12feeceb completed March 11, 2026, 10:38 p.m.
Created at: March 8, 2026, 2:59 p.m.