Triple

T5962966
Position Surface form Disambiguated ID Type / Status
Subject Gokstad ship E132682 entity
Predicate locatedIn P40 FINISHED
Object Vestfold E94296 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: Vestfold | Statement: [Gokstad ship, locatedIn, Vestfold]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vestfold
Context triple: [Gokstad ship, locatedIn, Vestfold]
  • A. Agder
    Agder is a county in southern Norway known for its long coastline, maritime heritage, and popular coastal towns and islands.
  • B. Vestfold og Telemark chosen
    Vestfold og Telemark is a former county in southeastern Norway known for its coastal towns, industrial heritage, and varied landscapes from fjords to inland forests and mountains.
  • C. Aust-Agder
    Aust-Agder was a former county in southern Norway known for its coastal towns, forests, and role in the country’s maritime and timber industries.
  • D. Sogn og Fjordane
    Sogn og Fjordane was a former county in western Norway known for its dramatic fjords, mountains, and coastal landscapes.
  • E. Hordaland
    Hordaland was a former county in western Norway known for its fjords, coastal landscapes, and the city of Bergen.
  • 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_69c0086c2364819091e9fe2f58fa2517 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c03a00e444819087f9df62263e83dc completed March 22, 2026, 6:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6e4036f2081909fa40e07d19291f2 completed March 27, 2026, 8:09 p.m.
Created at: March 22, 2026, 4:03 p.m.