Triple

T15073966
Position Surface form Disambiguated ID Type / Status
Subject Stockholm University E379950 entity
Predicate locatedIn P40 FINISHED
Object Stockholms län E3484 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: Stockholms län | Statement: [Stockholm University, locatedIn, Stockholms län]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Stockholms län
Context triple: [Stockholm University, locatedIn, Stockholms län]
  • A. Stockholm County chosen
    Stockholm County is a populous administrative region in east-central Sweden that includes the nation’s capital, Stockholm, and serves as a major political, economic, and cultural hub.
  • B. Uppsala County
    Uppsala County is an administrative region in east-central Sweden known for its historic university city of Uppsala and its mix of cultural heritage and rural landscapes.
  • C. Kalmar län
    Kalmar län is a county in southeastern Sweden known for its Baltic Sea coastline, historic towns, and the island of Öland.
  • D. Älvsborg County
    Älvsborg County was a former county in western Sweden that existed until 1997, when it was incorporated into the newly formed Västra Götaland County.
  • E. Södermanland County
    Södermanland County is an administrative region in east-central Sweden known for its mix of coastal landscapes, forests, and historic towns such as Nyköping and Eskilstuna.
  • 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_69d85cd7683881908d405c1b5d7b4f7f completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69dff7fa0570819088a97b28173154cd completed April 15, 2026, 8:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69febfdc8f64819083c7e3510e671b9a completed May 9, 2026, 5:02 a.m.
Created at: April 10, 2026, 3:02 a.m.