Triple

T10058233
Position Surface form Disambiguated ID Type / Status
Subject Aurich E208915 entity
Predicate hasTwinTown P919 FINISHED
Object Jelgava E132222 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: Jelgava | Statement: [Aurich, hasTwinTown, Jelgava]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jelgava
Context triple: [Aurich, hasTwinTown, Jelgava]
  • A. Jelgava chosen
    Jelgava is a city in central Latvia known for its historic Jelgava Palace and role as a regional cultural and educational center.
  • B. Ventspils
    Ventspils is a port city on Latvia’s Baltic Sea coast known for its major ice-free harbor, oil and cargo terminals, and well-preserved historic center.
  • C. Valmiera
    Valmiera is a historic city in northern Latvia, situated on the Gauja River and known today as a regional economic and cultural center in the Vidzeme region.
  • D. Daugavpils
    Daugavpils is Latvia’s second-largest city, known as the birthplace of abstract expressionist painter Mark Rothko and for its multicultural heritage and 19th-century fortress.
  • E. Jekabpils
    Jekabpils is a town in southeastern Latvia known for its historic architecture and scenic location along the Daugava 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_69ca836094408190a36a1ea7e9a86fcd completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cdcfaf7700819084dedf7b63e789c1 completed April 2, 2026, 2:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69d29a5d4b308190b5b1ece1ca99be86 completed April 5, 2026, 5:22 p.m.
Created at: March 30, 2026, 8:57 p.m.