Triple

T4744667
Position Surface form Disambiguated ID Type / Status
Subject Vilnius E105330 entity
Predicate historicalName P65 FINISHED
Object Wilno E14402 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: Wilno | Statement: [Vilnius, historicalName, Wilno]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wilno
Context triple: [Vilnius, historicalName, Wilno]
  • A. Wilno chosen
    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.
  • B. 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.
  • C. Białystok
    Białystok is a city in northeastern Poland best known as the birthplace of L. L. Zamenhof and the cradle of the international language Esperanto.
  • D. Warsaw
    Warsaw is the capital and largest city of Poland, known for its resilient history, especially its near-total destruction in World War II and subsequent postwar reconstruction.
  • E. Lwów
    Lwów is a historic city in Eastern Europe, now known as Lviv in western Ukraine, long recognized as a major cultural and political center of the region.
  • 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_69bd43ef87a48190a5bc3600711aa032 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd64aa72c0819082ede0f531d75e65 completed March 20, 2026, 3:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69be4d851fcc8190bb7ae007f8a5ce1d completed March 21, 2026, 7:49 a.m.
Created at: March 20, 2026, 1:19 p.m.