Triple

T4259562
Position Surface form Disambiguated ID Type / Status
Subject Villeurbanne E96069 entity
Predicate hasTwinTown P919 FINISHED
Object Velenje E310981 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: Velenje | Statement: [Villeurbanne, hasTwinTown, Velenje]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Velenje
Context triple: [Villeurbanne, hasTwinTown, Velenje]
  • A. Velenje chosen
    Velenje is a modern industrial town in northern Slovenia known for its coal mining heritage, large lakeside recreational area, and one of the largest Tito statues in the world.
  • B. Maribor
    Maribor is Slovenia’s second-largest city, known for its historic old town, wine culture, and the world’s oldest grapevine.
  • C. Sevnica
    Sevnica is a small town in central Slovenia known as the childhood home of former U.S. First Lady Melania Trump.
  • D. Kladno
    Kladno is an industrial city in the Czech Republic known historically for coal mining and steel production.
  • E. Ptuj
    Ptuj is one of Slovenia’s oldest towns, renowned for its well-preserved medieval architecture and rich cultural heritage along the Drava River.
  • 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_69b3454095ac81909c2494f7ff294af1 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b34f7fe7348190baed8d214268b756 completed March 12, 2026, 11:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5b78825508190b2b6ca46c8e1b27c completed March 14, 2026, 7:31 p.m.
Created at: March 12, 2026, 11:06 p.m.