Triple

T6393748
Position Surface form Disambiguated ID Type / Status
Subject Norwegian School of Economics E143890 entity
Predicate foundingLocation P40 FINISHED
Object Bergen E74082 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: Bergen | Statement: [Norwegian School of Economics, foundingLocation, Bergen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bergen
Context triple: [Norwegian School of Economics, foundingLocation, Bergen]
  • A. Bergen chosen
    Bergen is Norway's second-largest city, renowned for its historic harbor, surrounding mountains and fjords, and role as a former Hanseatic trading hub.
  • B. Bergen
    Bergen is a city in western Germany, historically notable as the site of the 1759 Battle of Bergen during the Seven Years' War.
  • C. Bergens
    The Bergens are a race of gloomy, troll-eating creatures who serve as the primary villains in the animated film "Trolls."
  • D. Trondheim
    Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
  • E. Stavanger
    Stavanger is a coastal city in southwestern Norway known for its oil industry hub status, historic wooden houses, and proximity to natural attractions like the Lysefjord and Preikestolen.
  • 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_69c008db906c819096f3597d55d95432 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c06881313481908e9082ffe57f29b6 completed March 22, 2026, 10:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7e4f308048190a5c42022e3f9e855 completed March 28, 2026, 2:25 p.m.
Created at: March 22, 2026, 4:34 p.m.