Triple

T6032196
Position Surface form Disambiguated ID Type / Status
Subject Hochsauerlandkreis E134331 entity
Predicate containsTown P847 FINISHED
Object Schmallenberg E537268 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: Schmallenberg | Statement: [Hochsauerlandkreis, containsTown, Schmallenberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Schmallenberg
Context triple: [Hochsauerlandkreis, containsTown, Schmallenberg]
  • A. Schmallenberg chosen
    Schmallenberg is a small town in the Hochsauerland district of North Rhine-Westphalia, Germany, known for its picturesque landscapes and tourism in the Sauerland region.
  • B. Vircava
    Vircava is a river in Latvia that serves as one of the tributaries feeding into the larger Lielupe River system.
  • C. Cervi
    Cervi is an Italian surname most notably associated with Al Cervi, a Hall of Fame American professional basketball player and coach.
  • D. Scherpenzeel
    Scherpenzeel is a small Dutch municipality in the province of Gelderland, known for its rural character and historic village center.
  • E. Mollerussa
    Mollerussa is a small town in the province of Lleida, Catalonia, Spain, known for its agricultural surroundings and regional commercial services.
  • 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.