Triple

T10373901
Position Surface form Disambiguated ID Type / Status
Subject Moss municipality E244452 entity
Predicate previouslyInCounty P65436 FINISHED
Object Østfold county E96296 NE FINISHED

How this triple was built (3 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: Østfold county | Statement: [Moss municipality, previouslyInCounty, Østfold county]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Østfold county
Context triple: [Moss municipality, previouslyInCounty, Østfold county]
  • A. Østfold chosen
    Østfold is a former county in southeastern Norway known for its coastal landscape along the Oslofjord, historic fortresses, and proximity to the Swedish border.
  • B. Akershus county
    Akershus county was a former county in southeastern Norway that historically surrounded Oslo and included both urban suburbs and rural areas before being merged into Viken county.
  • C. Agder
    Agder is a county in southern Norway known for its long coastline, maritime heritage, and popular coastal towns and islands.
  • D. 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.
  • E. Vestfold og Telemark
    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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: previouslyInCounty
Context triple: [Moss municipality, previouslyInCounty, Østfold county]
  • A. previousCounty chosen
    Indicates that one county was the immediately preceding county associated with an entity before the current or later county.
  • B. previouslyAdmitted
    Indicates that an entity has already been admitted or accepted at an earlier time prior to the current reference point.
  • C. inCounty
    Indicates that one entity is geographically or administratively located within the boundaries of a specified county.
  • D. hasNearbyFormerResidenceOf
    Indicates that one entity is located near a place that used to be the residence of another entity.
  • E. hasFormerJurisdictionOver
    Indicates that an entity previously held legal or administrative authority over another entity, but no longer does so.
  • F. None of above.

Provenance (4 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_69d381b3e328819094b23b8edcd29b5a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e9804e708190b15f5d38cac9c4c1 completed April 7, 2026, 11:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69d7fb98c52c8190a52682feacc2bd0d completed April 9, 2026, 7:18 p.m.
PD Predicate disambiguation batch_69d4dface5508190a7b42f01ad0a19a2 completed April 7, 2026, 10:42 a.m.
Created at: April 6, 2026, 12:02 p.m.