Triple

T1149336
Position Surface form Disambiguated ID Type / Status
Subject Akershus Fortress E23638 entity
Predicate near P350 FINISHED
Object Oslo harbor E28991 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 harbor | Statement: [Akershus Fortress, near, Oslo harbor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oslo harbor
Context triple: [Akershus Fortress, near, Oslo harbor]
  • A. Aker Brygge
    Aker Brygge is a popular waterfront district in Oslo known for its modern architecture, restaurants, shops, and vibrant harbor promenade.
  • B. Port of Oslo chosen
    The Port of Oslo is Norway’s largest and busiest seaport, serving as a key hub for passenger ferries, cargo traffic, and maritime trade in the Oslofjord region.
  • C. Port of Drammen
    The Port of Drammen is a key Norwegian seaport and logistics hub known especially for handling car imports and other cargo for the Oslofjord region.
  • D. Kolsås
    Kolsås is a suburban area in Bærum, Norway, known as the endpoint of one of the Oslo Metro lines and for its nearby forested hill popular for hiking and climbing.
  • E. Akershus
    Akershus is a historical county in southeastern Norway that encompassed areas around the capital Oslo and played a key role in the region’s administrative and military history.
  • 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_69a493f0d32c8190ac74bad3c87f2641 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bc7190308190ab104480ed208b22 completed March 1, 2026, 10:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbf13304881908aa74d92ef7b1c86 completed March 8, 2026, 12:13 a.m.
Created at: March 1, 2026, 7:44 p.m.