Triple

T11743604
Position Surface form Disambiguated ID Type / Status
Subject Canton of Aargau E279213 entity
Predicate city P40 FINISHED
Object Lenzburg E796598 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: Lenzburg | Statement: [Canton of Aargau, city, Lenzburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lenzburg
Context triple: [Canton of Aargau, city, Lenzburg]
  • A. Lenzburg chosen
    Lenzburg is a historic Swiss town in the canton of Aargau, known for its medieval hilltop castle and well-preserved old town.
  • B. Salzburg
    Salzburg is a historic Austrian city on the Salzach River, renowned for its baroque architecture, Alpine setting, and as the birthplace of composer Wolfgang Amadeus Mozart.
  • C. Gmunden
    Gmunden is a picturesque town in Upper Austria known for its lakeside setting on the Traunsee and its historic ceramics industry.
  • D. Linz
    Linz is a major Austrian city known for its industrial heritage, vibrant cultural scene, and location along the Danube River.
  • E. Innsbruck
    Innsbruck is a city in western Austria known for its Alpine setting and winter sports facilities, and it later successfully hosted the Winter Olympics in 1964 and 1976.
  • 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_69d6ab01038c819080714901502c84fc completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a4f191388190bd6ef7e80c41ca48 completed April 10, 2026, 7:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69f67c5c80cc819099b0ad7a2911781c completed May 2, 2026, 10:36 p.m.
Created at: April 8, 2026, 9:41 p.m.