Triple

T5705898
Position Surface form Disambiguated ID Type / Status
Subject Royal Guards E125782 entity
Predicate basedIn P40 FINISHED
Object Oslo, Norway E3654 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: Oslo, Norway | Statement: [Royal Guards, basedIn, Oslo, Norway]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oslo, Norway
Context triple: [Royal Guards, basedIn, Oslo, Norway]
  • A. Oslo chosen
    Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
  • B. Trondheim
    Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
  • C. Bergen
    Bergen is a city in western Germany, historically notable as the site of the 1759 Battle of Bergen during the Seven Years' War.
  • D. Bergen
    Bergen is Norway's second-largest city, renowned for its historic harbor, surrounding mountains and fjords, and role as a former Hanseatic trading hub.
  • 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_69c0082d6fe48190b777fb383769e5c8 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02459cd18819080fda0b481d11f08 completed March 22, 2026, 5:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c11ca111188190932a17a2fab097c7 completed March 23, 2026, 10:57 a.m.
Created at: March 22, 2026, 3:45 p.m.