Triple

T15000915
Position Surface form Disambiguated ID Type / Status
Subject Musical Theatre in Gdynia E374084 entity
Predicate operator P179 FINISHED
Object City of Gdynia E12134 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: City of Gdynia | Statement: [Musical Theatre in Gdynia, operator, City of Gdynia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Gdynia
Context triple: [Musical Theatre in Gdynia, operator, City of Gdynia]
  • A. Gdynia chosen
    Gdynia is a major seaport city on Poland’s Baltic coast, developed rapidly in the 20th century into one of the country’s key maritime and economic centers.
  • B. Gdańsk
    Gdańsk is a major Polish port city on the Baltic Sea, known for its rich Hanseatic history, shipyards, and role in the origins of the Solidarity movement.
  • C. Bydgoszcz
    Bydgoszcz is a major city in northern Poland known as an important economic, cultural, and academic center on the Brda and Vistula rivers.
  • D. Serock
    Serock is a locality situated near the Western Bug River in eastern Europe.
  • E. Sopot
    Sopot is a suburban municipality of Belgrade, Serbia, known for its rural character and proximity to the Avala and Kosmaj mountains.
  • 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_69d85ccc84388190aa151e5173370c8d completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded72fec948190b1c9705538c57976 completed April 15, 2026, 12:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff755bbe608190a9a565218eee7005 completed May 9, 2026, 5:56 p.m.
Created at: April 10, 2026, 2:54 a.m.