Triple

T14009766
Position Surface form Disambiguated ID Type / Status
Subject Nagykanizsa E337046 entity
Predicate hasTwinTown P919 FINISHED
Object Zadar E25849 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: Zadar | Statement: [Nagykanizsa, hasTwinTown, Zadar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zadar
Context triple: [Nagykanizsa, hasTwinTown, Zadar]
  • A. Zadar chosen
    Zadar is a historic coastal city in Croatia on the Adriatic Sea, known for its Roman and Venetian ruins, medieval churches, and modern seaside installations like the Sea Organ.
  • B. Opatija
    Opatija is a historic seaside resort town on Croatia’s Adriatic coast, known for its elegant Austro-Hungarian architecture, mild climate, and long tradition of tourism.
  • C. Rijeka
    Rijeka is a significant Croatian port city on the Adriatic Sea, known for its maritime industry, cultural heritage, and role as a key transport hub.
  • D. Šibenik
    Šibenik is a historic coastal city in Croatia known for its medieval architecture and the UNESCO-listed Cathedral of St. James.
  • E. Rovinj
    Rovinj is a picturesque coastal town on Croatia’s Istrian peninsula, known for its colorful old town, fishing harbor, and popular seaside tourism.
  • 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_69d81c645c5c8190b1fd16a285a1b78a completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2ed44f90819099ad08c09c066b56 completed April 14, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd466d5f1c81909accae028184b857 completed May 8, 2026, 2:11 a.m.
Created at: April 9, 2026, 10:19 p.m.