Triple

T10428771
Position Surface form Disambiguated ID Type / Status
Subject Ål E245853 entity
Predicate partOf P40 FINISHED
Object Hallingdal district E107387 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: Hallingdal district | Statement: [Ål, partOf, Hallingdal district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hallingdal district
Context triple: [Ål, partOf, Hallingdal district]
  • A. Ringerike district
    Ringerike district is a historic region in southeastern Norway known for its cultural heritage, distinctive landscape, and early medieval significance.
  • B. Hallingdal chosen
    Hallingdal is a major valley and traditional district in southeastern Norway, known for its river, ski resorts, and rich folk culture.
  • C. Sagene district
    Sagene district is a central borough of Oslo, Norway, known for its historic industrial areas, riverside parks, and vibrant urban neighborhoods.
  • D. Sunnmøre district
    Sunnmøre district is a coastal region in the southwestern part of Møre og Romsdal county in Norway, known for its fjords, islands, and maritime communities.
  • E. 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.
  • 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_69d381bf3dc08190bf35a2643e4e8f22 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4ea4b4b5881908ae23f8efeea482b completed April 7, 2026, 11:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69dbd966f0f08190a60ca3bcf0e08e98 completed April 12, 2026, 5:41 p.m.
Created at: April 6, 2026, 12:13 p.m.