Triple

T22641111
Position Surface form Disambiguated ID Type / Status
Subject Wilhelm von Kaulbach E558827 entity
Predicate placeOfBirth P1 FINISHED
Object Arolsen 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: Arolsen | Statement: [Wilhelm von Kaulbach, placeOfBirth, Arolsen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arolsen
Context triple: [Wilhelm von Kaulbach, placeOfBirth, Arolsen]
  • A. Arolsen chosen
    Arolsen is a historic town in the German state of Hesse, known for its baroque architecture and as the former residence of the princes of Waldeck and Pyrmont.
  • B. Buchenwald
    Buchenwald was one of Nazi Germany’s largest and most notorious concentration camps, where tens of thousands of prisoners were subjected to forced labor, brutal conditions, and mass murder during the Holocaust.
  • C. Anhausen
    Anhausen is a small German village best known as the birthplace of professional golfer Bernhard Langer.
  • D. Dachau
    Dachau was one of Nazi Germany’s first and most infamous concentration camps, serving as a model for the camp system and a site of widespread persecution, forced labor, and mass murder during the Holocaust.
  • E. Geisenhausen
    Geisenhausen is a market town in Lower Bavaria, Germany, known for its rural character and proximity to the city of Landshut.
  • 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_69e24547f7fc819086e2c4ba3b979657 completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f170116fe881908178cffef26e3ae7 completed April 29, 2026, 2:42 a.m.
Created at: April 17, 2026, 3:04 p.m.