Triple

T8929906
Position Surface form Disambiguated ID Type / Status
Subject Södertälje E212624 entity
Predicate hasTwinTown P919 FINISHED
Object Sønderborg E521588 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: Sønderborg | Statement: [Södertälje, hasTwinTown, Sønderborg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sønderborg
Context triple: [Södertälje, hasTwinTown, Sønderborg]
  • A. Sønderborg chosen
    Sønderborg is a coastal town in southern Denmark known for its historic castle, waterfront setting on the island of Als, and role as a regional cultural and educational center.
  • B. Svendborg
    Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
  • C. Vordingborg
    Vordingborg is a historic coastal town in southern Denmark known for the ruins of Vordingborg Castle and its prominent Goose Tower.
  • D. Skanderborg
    Skanderborg is a Danish town in Jutland known for its lakeside setting and annual music festival, Smukfest.
  • E. Nyborg
    Nyborg is a historic coastal town and former royal seat in central Denmark, located on the island of Funen.
  • 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_69ca8395c438819087d7cb844ab5990c completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6676d5d881908ce78cbb5561a68b completed April 1, 2026, 12:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69d19f50f1c4819099a9c511f58e9873 completed April 4, 2026, 11:31 p.m.
Created at: March 30, 2026, 6:57 p.m.