Triple

T3613413
Position Surface form Disambiguated ID Type / Status
Subject Kościuszko Square E76541 entity
Predicate hasNearbyAttraction P2064 FINISHED
Object Marina 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: Marina Gdynia | Statement: [Kościuszko Square, hasNearbyAttraction, Marina Gdynia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marina Gdynia
Context triple: [Kościuszko Square, hasNearbyAttraction, Marina 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. Sopot
    Sopot is a suburban municipality of Belgrade, Serbia, known for its rural character and proximity to the Avala and Kosmaj mountains.
  • C. Sopot
    Sopot is a Polish Baltic Sea resort city famous for its sandy beaches, long wooden pier, and vibrant spa and nightlife culture.
  • D. 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.
  • E. Port of Szczecin
    The Port of Szczecin is a major Polish seaport on the Oder River near the Baltic Sea, serving as an important hub for maritime trade and inland waterway transport in northwestern Poland.
  • 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_69ad85da0ba481908b3b48c69efe2b98 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc2786f808190be4e42734a79d74e completed March 8, 2026, 6:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69b44f068e188190bb8c4e098e3153b3 completed March 13, 2026, 5:53 p.m.
Created at: March 8, 2026, 3:23 p.m.