Triple

T6565536
Position Surface form Disambiguated ID Type / Status
Subject Christiania E153896 entity
Predicate predecessor P97 FINISHED
Object Oslo (medieval city name) 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 (medieval city name) | Statement: [Christiania, predecessor, Oslo (medieval city name)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oslo (medieval city name)
Context triple: [Christiania, predecessor, Oslo (medieval city name)]
  • A. Trondheim
    Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
  • B. Fredrikstad
    Fredrikstad is a coastal city in southeastern Norway known for its well-preserved fortified old town and role as a regional educational and commercial center.
  • C. Oslo chosen
    Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
  • D. Gamle Oslo district
    Gamle Oslo district is a central borough of Norway’s capital city known for its historic neighborhoods, diverse population, and rapidly developing waterfront areas.
  • 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_69c6880cb35881909b763eb0125236b9 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ae3cc05881908e943d3f7f8a2b1d completed March 27, 2026, 4:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6d5622e0481909b0ac0f4e06d19bc completed March 27, 2026, 7:07 p.m.
Created at: March 27, 2026, 1:52 p.m.