Triple

T4274056
Position Surface form Disambiguated ID Type / Status
Subject Lake Zug E97004 entity
Predicate hasShorelineTown P33430 FINISHED
Object Zug E187177 NE FINISHED

How this triple was built (3 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: Zug | Statement: [Lake Zug, hasShorelineTown, Zug]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Zug
Context triple: [Lake Zug, hasShorelineTown, Zug]
  • A. Zug chosen
    Zug is a small, affluent Swiss city and canton known for its low taxes, picturesque lakeside setting, and role as a hub for international businesses and cryptocurrency companies.
  • B. Olten
    Olten is a town in the canton of Solothurn in northwestern Switzerland, known as an important railway junction and regional economic center.
  • C. Kloten
    Kloten is a town in the canton of Zurich in northern Switzerland, best known as the home of Zurich Airport.
  • D. Zurich
    Zurich is the largest city in Switzerland, known as a global financial hub and cultural center situated on the shores of Lake Zurich.
  • E. Schaffhausen
    Schaffhausen is a historic town and capital of the canton of the same name in northern Switzerland, known for its well-preserved medieval old town and proximity to the Rhine Falls.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasShorelineTown
Context triple: [Lake Zug, hasShorelineTown, Zug]
  • A. hasShorelineCommunity chosen
    Indicates that a place or region includes or is associated with a community located along its shoreline.
  • B. hasShorelineUse
    Indicates that a geographic area or property is used for a particular type of activity or purpose along its shoreline.
  • C. hasShoreOn
    Indicates that one geographic entity borders or is directly adjacent to the shore of another body of water.
  • D. hasShoreFeature
    Indicates that a shore or coastline possesses a specific physical or environmental feature.
  • E. hasShorelineCountry
    Indicates that a country possesses a coastline or land boundary directly adjacent to a particular body of water or coastal region.
  • F. None of above.

Provenance (4 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_69b34544be3c819084d1ab82d29f90c5 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3501abb74819086b2f04ac7a5c114 completed March 12, 2026, 11:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5c71fc0bc8190922ae85d3bd23546 completed March 14, 2026, 8:37 p.m.
PD Predicate disambiguation batch_69b347faa45481908c19c29fb906dc92 completed March 12, 2026, 11:10 p.m.
Created at: March 12, 2026, 11:07 p.m.