Triple

T18980190
Position Surface form Disambiguated ID Type / Status
Subject Seetal E464399 entity
Predicate railwayConnectsTo P109198 FINISHED
Object Lenzburg 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: Lenzburg | Statement: [Seetal, railwayConnectsTo, Lenzburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lenzburg
Context triple: [Seetal, railwayConnectsTo, 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 (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_69d8dd008af48190a97ff1c6488edf1b completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5d65b573881908575e61a62b70787 completed April 20, 2026, 7:31 a.m.
Created at: April 10, 2026, 12:01 p.m.