Triple

T21368932
Position Surface form Disambiguated ID Type / Status
Subject canton of Zug E526997 entity
Predicate hasMunicipality P847 FINISHED
Object Steinhausen 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: Steinhausen | Statement: [canton of Zug, hasMunicipality, Steinhausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Steinhausen
Context triple: [canton of Zug, hasMunicipality, Steinhausen]
  • A. Steinhausen chosen
    Steinhausen is a municipality in the canton of Zug in central Switzerland, known for its residential character and proximity to the city of Zug.
  • B. Starnberg
    Starnberg is a lakeside town in Bavaria, Germany, known for its affluent residential character and scenic location on Lake Starnberg southwest of Munich.
  • C. Tennstädt
    Tennstädt is a small town in the Thuringia region of central Germany.
  • D. Regenstauf
    Regenstauf is a market town in the Upper Palatinate region of Bavaria, Germany, situated north of the city of Regensburg along the river Regen.
  • E. Kottenheim
    Kottenheim is a small municipality in western Germany’s Rhineland-Palatinate region, known for its volcanic landscape and traditional stone quarrying.
  • 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_69e0b51e80808190ba5cb05667af02a9 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69ee5baf5fb4819093f8d8afdd83ffdb completed April 26, 2026, 6:38 p.m.
Created at: April 16, 2026, 5:09 p.m.