Triple

T16627521
Position Surface form Disambiguated ID Type / Status
Subject Zułowo E403986 entity
Predicate locatedNear P294 FINISHED
Object Švenčionys E209675 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: Švenčionys | Statement: [Zułowo, locatedNear, Švenčionys]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Švenčionys
Context triple: [Zułowo, locatedNear, Švenčionys]
  • A. Švenčionys chosen
    Švenčionys is a small historic town in eastern Lithuania known for its multicultural past and former Jewish community.
  • B. Vilkaviškis
    Vilkaviškis is a town in southwestern Lithuania known as an administrative and historical center of the surrounding agricultural region.
  • C. Zarasai
    Zarasai is a small town in northeastern Lithuania known for its lakes and scenic natural surroundings.
  • D. Sakiai
    Sakiai is a small town in southwestern Lithuania known for its proximity to the Russian and Polish borders and its role as a local administrative and cultural center.
  • E. Rokiškis
    Rokiškis is a town in northeastern Lithuania known for its well-preserved manor, historic architecture, and role as a regional cultural center.
  • 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_69d883897eb481909eaaa088ba9918d9 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e378e2683481908c9a5ed895a09a6e completed April 18, 2026, 12:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a007dba91bc819090a78ac4c0c01fc8 completed May 10, 2026, 12:44 p.m.
Created at: April 10, 2026, 5:17 a.m.