Triple

T5670813
Position Surface form Disambiguated ID Type / Status
Subject Treaty of Roskilde E124970 entity
Predicate cededTerritory P13709 FINISHED
Object Trøndelag E17971 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: Trøndelag | Statement: [Treaty of Roskilde, cededTerritory, Trøndelag]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Trøndelag
Context triple: [Treaty of Roskilde, cededTerritory, Trøndelag]
  • A. Trøndelag chosen
    Trøndelag is a central region of Norway known for its historic city of Trondheim, coastal landscapes, and strong cultural traditions.
  • B. Buskerud
    Buskerud is a former county in southeastern Norway known for its varied landscape of forests, rivers, and mountains, including parts of the Hallingdal valley and Hardangervidda plateau.
  • C. Møre og Romsdal
    Møre og Romsdal is a coastal county in western Norway known for its dramatic fjords, islands, and mountainous landscapes.
  • D. Nord-Valdres
    Nord-Valdres is the northern part of the traditional Valdres district in Innlandet county, Norway, known for its mountainous landscapes, valleys, and rural communities.
  • E. Hedmarken
    Hedmarken is a traditional district in Innlandet county in eastern Norway, known for its agricultural landscapes and its central town, Hamar.
  • 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_69c00828906881908966f270b8f130cf completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02349b570819090f754a2f25e4cf3 completed March 22, 2026, 5:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69c51892bb648190987b9626ef7ea4d9 completed March 26, 2026, 11:29 a.m.
Created at: March 22, 2026, 3:43 p.m.