Triple

T5943502
Position Surface form Disambiguated ID Type / Status
Subject Jelgava E132222 entity
Predicate hasTwinTown P919 FINISHED
Object Herning E51183 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: Herning | Statement: [Jelgava, hasTwinTown, Herning]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Herning
Context triple: [Jelgava, hasTwinTown, Herning]
  • A. Herning chosen
    Herning is a Danish city in the Central Jutland region known for its trade fairs, conference facilities, and vibrant cultural and sports events.
  • B. Horsens
    Horsens is a city in eastern Jutland, Denmark, known historically as a market and industrial town and as the birthplace of explorer Vitus Bering.
  • C. Kolding
    Kolding is a historic Danish city in Southern Jutland known for Koldinghus Castle, its fjord-side location, and its role as a regional cultural and educational center.
  • D. Randers
    Randers is a historic market town and one of the largest cities in eastern Jutland, Denmark, known for its old town center and location along the Gudenå River.
  • E. Hellerup
    Hellerup is a suburban district just north of central Copenhagen, known for its affluent residential areas, seaside location, and role as a key transport and commercial hub.
  • 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_69c00869d3308190af89b2453e0f7546 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c0393641d0819081c6c44816d94e4e completed March 22, 2026, 6:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69c1355102c88190b139ba052e12a875 completed March 23, 2026, 12:42 p.m.
Created at: March 22, 2026, 4:01 p.m.