Triple

T14609795
Position Surface form Disambiguated ID Type / Status
Subject Kurhessen E342926 entity
Predicate hasSubregion P285 FINISHED
Object Witzenhausen E487665 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: Witzenhausen | Statement: [Kurhessen, hasSubregion, Witzenhausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Witzenhausen
Context triple: [Kurhessen, hasSubregion, Witzenhausen]
  • A. Witzenhausen chosen
    Witzenhausen is a small town in northern Hesse, Germany, known for its cherry orchards and agricultural research institutions.
  • B. Willingshausen
    Willingshausen is a small municipality in central Germany known for its historic artists’ colony and rural cultural heritage.
  • C. Ehringshausen
    Ehringshausen is a municipality in the Lahn-Dill district of the German state of Hesse.
  • D. Schwickershausen
    Schwickershausen is a village district of the spa town Bad Camberg in the Hesse region of Germany.
  • E. Vellinghausen
    Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
  • 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_69d822dec68081908c2553145c4051dc completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb44f0dd48190a78662b5998a6722 completed April 14, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff13322c548190bac21db2bfa56ee9 completed May 9, 2026, 10:57 a.m.
Created at: April 10, 2026, 1:25 a.m.