Triple

T18112967
Position Surface form Disambiguated ID Type / Status
Subject Etnedal E433527 entity
Predicate partOf P40 FINISHED
Object Valdres district 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: Valdres district | Statement: [Etnedal, partOf, Valdres district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Valdres district
Context triple: [Etnedal, partOf, Valdres district]
  • A. Valdres chosen
    Valdres is a scenic valley and traditional district in central southern Norway, known for its mountains, lakes, and rich cultural heritage.
  • B. Surselva District
    Surselva District is an administrative district in the canton of Graubünden in eastern Switzerland, known for its Romansh-speaking communities and Alpine landscapes.
  • C. Illertissen region
    The Illertissen region is an area in southern Germany known for its location along the Iller River and its mix of small towns, agricultural landscapes, and local industry.
  • D. Ofoten district
    Ofoten district is a traditional region in Nordland county in northern Norway, known for its fjords, mountains, and the town of Narvik as its main urban center.
  • E. Horgen District
    Horgen District is an administrative district in the canton of Zurich, Switzerland, located along the western shore of Lake Zurich and encompassing a mix of suburban and rural communities.
  • 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_69d8b90916008190a1f110bd7ced5473 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4ddd3fd9c81909bfe95927f7553e3 completed April 19, 2026, 1:51 p.m.
Created at: April 10, 2026, 10:28 a.m.