Triple

T786364
Position Surface form Disambiguated ID Type / Status
Subject NOR E16811 entity
Predicate countryNameLocal P19618 FINISHED
Object Noreg E2896 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: Noreg | Statement: [NOR, countryNameLocal, Noreg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Noreg
Context triple: [NOR, countryNameLocal, Noreg]
  • A. Norway chosen
    Norway is a Nordic country in Northern Europe known for its high standard of living, extensive welfare state, and dramatic natural landscapes of fjords, mountains, and coastline.
  • B. Nord
    Nord is a department in northern France known for its industrial heritage, dense population, and proximity to Belgium.
  • C. Vestland
    Vestland is a county in western Norway known for its dramatic fjords, coastal landscapes, and the city of Bergen.
  • D. Denmark–Norway
    Denmark–Norway was an early modern dual monarchy uniting the kingdoms of Denmark and Norway (including their overseas territories) under a single crown from the 16th to the early 19th century.
  • E. Denmark
    Denmark is a Nordic country in Northern Europe known for its high standard of living, strong welfare state, and role as a founding member of NATO and the United Nations.
  • 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_69a4936cb7448190914f5fe4b8d81607 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4aa9e0f0081909d2a89387d6c08e1 completed March 1, 2026, 9:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac82e8bd788190a20a580bae9bd94e completed March 7, 2026, 7:56 p.m.
Created at: March 1, 2026, 7:38 p.m.