Triple

T17182421
Position Surface form Disambiguated ID Type / Status
Subject Georg Büchner E417015 entity
Predicate hasWork P6260 FINISHED
Object Lenz E841991 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: Lenz | Statement: [Georg Büchner, hasWork, Lenz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lenz
Context triple: [Georg Büchner, hasWork, Lenz]
  • A. Lenz chosen
    Lenz is a German surname most notably associated with the 18th-century Sturm und Drang writer Jakob Michael Reinhold Lenz.
  • B. Lentz
    Lentz is a surname most notably associated with Irene Lentz, an influential American fashion and costume designer in Hollywood’s Golden Age.
  • C. Gouy
    Gouy is a small commune in northern France located within the administrative boundaries of the canton of Le Cateau-Cambrésis.
  • D. Lindemann
    Lindemann is a German surname most notably associated with Ferdinand von Lindemann, the mathematician who proved that π is a transcendental number.
  • E. Néel
    Néel is a French surname most notably associated with physicist Louis Néel, a Nobel laureate recognized for his pioneering work in magnetism.
  • 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_69d886d5f34c8190b24564dfaa63f3fb completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42d934ec08190acc47073758ac3c0 completed April 19, 2026, 1:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a015fca04cc8190a9df230078fbe268 completed May 11, 2026, 4:49 a.m.
Created at: April 10, 2026, 5:37 a.m.