Triple

T1300155
Position Surface form Disambiguated ID Type / Status
Subject Södermanland County E27742 entity
Predicate hasUrbanArea P316 FINISHED
Object Oxelösund E158065 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: Oxelösund | Statement: [Södermanland County, hasUrbanArea, Oxelösund]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oxelösund
Context triple: [Södermanland County, hasUrbanArea, Oxelösund]
  • A. Oxelösund chosen
    Oxelösund is a small coastal industrial town in eastern Sweden known for its port facilities and steel production.
  • B. Trångsund
    Trångsund is a suburban district in the southern part of the Stockholm urban area in Sweden, known for its residential character and proximity to lakes and nature.
  • C. Hovsjö
    Hovsjö is a residential district in the city of Södertälje, Sweden, known for its large-scale housing estates and diverse population.
  • D. Muskö
    Muskö is an island and locality in the Stockholm archipelago of Sweden, known for its naval base and scenic coastal environment.
  • E. Brunnsviken
    Brunnsviken is a scenic bay in the Stockholm area of Sweden, known for its surrounding parks, recreational areas, and cultural landmarks.
  • 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_69a496d6682881909ba658f1c1e0e2b0 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c11314a48190ab4efb8b1acdce50 completed March 1, 2026, 10:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69acde0ffa188190a073650bb595bb16 completed March 8, 2026, 2:25 a.m.
Created at: March 1, 2026, 7:51 p.m.