Triple

T3525927
Position Surface form Disambiguated ID Type / Status
Subject Drava E74537 entity
Predicate flowsThroughCity P10456 FINISHED
Object Maribor E114358 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: Maribor | Statement: [Drava, flowsThroughCity, Maribor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maribor
Context triple: [Drava, flowsThroughCity, Maribor]
  • A. Maribor chosen
    Maribor is Slovenia’s second-largest city, known for its historic old town, wine culture, and the world’s oldest grapevine.
  • B. Ljubljana
    Ljubljana is the capital and largest city of Slovenia, known for its picturesque old town, Baroque and Art Nouveau architecture, and vibrant cultural scene along the Ljubljanica River.
  • C. Velenje
    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.
  • D. Novo Mesto, Slovenia
    Novo Mesto is a historic town in southeastern Slovenia known for its cultural heritage and picturesque setting on the Krka River.
  • E. Sevnica
    Sevnica is a small town in central Slovenia known as the childhood home of former U.S. First Lady Melania Trump.
  • 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_69ad85d0c5488190a3d8e02ebd01a1aa completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbc6a8d0c819094d38b9c47fb67b4 completed March 8, 2026, 6:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69b37e8de2648190809369c3f0b7d85d completed March 13, 2026, 3:03 a.m.
Created at: March 8, 2026, 3:19 p.m.