Triple

T6032197
Position Surface form Disambiguated ID Type / Status
Subject Hochsauerlandkreis E134331 entity
Predicate containsTown P847 FINISHED
Object Sundern E228422 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: Sundern | Statement: [Hochsauerlandkreis, containsTown, Sundern]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sundern
Context triple: [Hochsauerlandkreis, containsTown, Sundern]
  • A. Sundern chosen
    Sundern is a town in the Hochsauerland district of North Rhine-Westphalia, Germany, known for its proximity to the Sorpe Dam and the surrounding Sauerland recreational region.
  • B. Winsum
    Winsum is a historic village and former municipality in the Dutch province of Groningen, known for its old churches, windmills, and picturesque canals.
  • C. Nottuln
    Nottuln is a historic municipality in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
  • D. Starnberg
    Starnberg is a lakeside town in Bavaria, Germany, known for its affluent residential character and scenic location on Lake Starnberg southwest of Munich.
  • E. Landsberg
    Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
  • 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_69c0087515148190a97475d412563865 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c056b0a8d081909035e2e85e851ca1 completed March 22, 2026, 8:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69c113855ad08190b9ff826a2f39c356 completed March 23, 2026, 10:18 a.m.
Created at: March 22, 2026, 4:08 p.m.