Triple

T10687873
Position Surface form Disambiguated ID Type / Status
Subject Östergötland County E251928 entity
Predicate containsCity P294 FINISHED
Object Mjölby E864236 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: Mjölby | Statement: [Östergötland County, containsCity, Mjölby]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mjölby
Context triple: [Östergötland County, containsCity, Mjölby]
  • A. Mjölby chosen
    Mjölby is a small Swedish town known for its agricultural surroundings and location in the southern part of Östergötland County.
  • B. Ronneby
    Ronneby is a historic town in southern Sweden known for its well-preserved wooden architecture, spa traditions, and scenic location in Blekinge County.
  • C. Bollstanäs
    Bollstanäs is a residential locality in Sweden situated within the suburban area of Upplands Väsby, north of Stockholm.
  • D. Bollnäs
    Bollnäs is a small Swedish town known for its scenic lakeside setting, traditional wooden architecture, and strong bandy sports culture.
  • E. Ljungby
    Ljungby is a small Swedish town in southern Småland known for its lakeside surroundings, forestry-based economy, and role as a local commercial and cultural center.
  • 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_69d6aa5bd7c08190a816e733b4045c23 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6fd19f0f481909eeaa75d17d9c060 completed April 9, 2026, 1:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69d9889d1f988190938be54771161b00 completed April 10, 2026, 11:32 p.m.
Created at: April 8, 2026, 9:11 p.m.