Triple

T17102579
Position Surface form Disambiguated ID Type / Status
Subject Lünen E415014 entity
Predicate hasTwinTown P919 FINISHED
Object Panevėžys, Lithuania E94346 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: Panevėžys, Lithuania | Statement: [Lünen, hasTwinTown, Panevėžys, Lithuania]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Panevėžys, Lithuania
Context triple: [Lünen, hasTwinTown, Panevėžys, Lithuania]
  • A. Panevėžys chosen
    Panevėžys is a major city in northern Lithuania known as an important regional industrial and cultural center.
  • B. Panemunė, Lithuania
    Panemunė is a town in southwestern Lithuania situated on the banks of the Neman River, directly across from Sovetsk, Russia, and known as a border crossing point between the two countries.
  • C. Vilkaviškis
    Vilkaviškis is a town in southwestern Lithuania known as an administrative and historical center of the surrounding agricultural region.
  • D. Joniškis
    Joniškis is a small town in northern Lithuania known for its historic architecture and cultural heritage, including well-preserved synagogues.
  • E. Radviliškis
    Radviliškis is a town in northern Lithuania known as a regional railway hub and administrative center within Šiauliai County.
  • 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_69d886cfc8e88190b05ba466edd35591 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dc2495c88190b5b16a006a994faf completed April 18, 2026, 7:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0170e153808190b2a253f64da87737 completed May 11, 2026, 6:02 a.m.
Created at: April 10, 2026, 5:35 a.m.