Triple

T6708628
Position Surface form Disambiguated ID Type / Status
Subject Alytus E153072 entity
Predicate country P26 FINISHED
Object Lithuania E17692 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: Lithuania | Statement: [Alytus, country, Lithuania]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lithuania
Context triple: [Alytus, country, Lithuania]
  • A. Lithuania chosen
    Lithuania is a Baltic nation in Northern Europe known for its medieval history, restored independence from the Soviet Union in 1990, and membership in both the European Union and NATO.
  • B. Latvia
    Latvia is a Baltic nation in Northern Europe known for its historic capital Riga, diverse cultural heritage, and membership in major international organizations such as the European Union and NATO.
  • C. Dzūkija
    Dzūkija is a historical ethnographic region in southeastern Lithuania known for its extensive forests, traditional rural culture, and distinctive dialect.
  • D. Latveria
    Latveria is a fictional Eastern European nation in Marvel Comics, best known as the technologically advanced yet oppressive kingdom ruled by the supervillain Doctor Doom.
  • E. Estonia
    Estonia is a Northern European country on the Baltic Sea known for its advanced digital society, rapid post-Soviet economic development, and membership in organizations such as the European Union and NATO.
  • 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_69c68808d8d8819087369015270788fe completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d1049b7c8190a970a165d15b440b completed March 27, 2026, 6:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69c700772aa48190a1356b5a252f6524 completed March 27, 2026, 10:11 p.m.
Created at: March 27, 2026, 2:06 p.m.