Triple

T13230659
Position Surface form Disambiguated ID Type / Status
Subject Murten E315007 entity
Predicate hasPart P35 FINISHED
Object Murten old town E315007 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: Murten old town | Statement: [Murten, hasPart, Murten old town]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Murten old town
Context triple: [Murten, hasPart, Murten old town]
  • A. Murten chosen
    Murten is a historic bilingual town in the canton of Fribourg, Switzerland, known for its well-preserved medieval old town and lakeside setting on Lake Murten.
  • B. Old Town of Bern
    The Old Town of Bern is a well-preserved medieval city center and UNESCO World Heritage Site in Switzerland, known for its arcaded streets, sandstone buildings, and historic landmarks along a loop of the Aare River.
  • C. Old Town of Zurich
    The Old Town of Zurich is the historic city center characterized by medieval streets, riverside promenades, and well-preserved architecture, serving as a cultural and tourist heart of Zurich.
  • D. Thun Old Town
    Thun Old Town is the historic medieval center of Thun, Switzerland, known for its cobbled streets, riverside setting, and landmark hilltop castle.
  • E. Old Town of Lausanne
    The Old Town of Lausanne is the historic city center characterized by medieval streets, traditional architecture, and lively squares clustered around the hilltop cathedral.
  • 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_69d806affc688190a25b6ccc588e9c72 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98d336ae08190bfc118cfbefddf84 completed April 10, 2026, 11:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6ff2c07488190ad07c544cca63a7d completed May 3, 2026, 7:54 a.m.
Created at: April 9, 2026, 9:21 p.m.