Triple

T2916964
Position Surface form Disambiguated ID Type / Status
Subject Orłowo E78629 entity
Predicate partOf P40 FINISHED
Object Tricity 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: Tricity | Statement: [Orłowo, partOf, Tricity]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tricity
Context triple: [Orłowo, partOf, Tricity]
  • A. Warsaw metropolitan area
    The Warsaw metropolitan area is the large urban agglomeration centered on Poland’s capital city, encompassing Warsaw and its surrounding suburban and commuter zones.
  • B. Łeba
    Łeba is a river in northern Poland that flows through the Pomeranian region to the Baltic Sea.
  • C. Lublin
    Lublin is a historic city in eastern Poland known as a major cultural, academic, and economic center and for its significant role in Polish political history.
  • D. 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.
  • E. Wilno
    Wilno is the historical Polish name for Vilnius, a major cultural and political center of the region that served as an important city in the interwar Second Polish Republic.
  • 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_69ad8b0c2ad081909ff87050ae542bb9 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad96a2bdd88190aa01c26a27f9afbd completed March 8, 2026, 3:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69b0562847488190a0d6b7de99796cd9 completed March 10, 2026, 5:34 p.m.
Created at: March 8, 2026, 2:53 p.m.