Triple

T6178131
Position Surface form Disambiguated ID Type / Status
Subject Pilar E137871 entity
Predicate country P26 FINISHED
Object Paraguay E28339 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: Paraguay | Statement: [Pilar, country, Paraguay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paraguay
Context triple: [Pilar, country, Paraguay]
  • A. Paraguay chosen
    Paraguay is a landlocked country in central South America known for its bilingual Spanish and Guaraní culture and its location along the Paraguay and Paraná rivers.
  • B. Argentina and Paraguay
    Argentina and Paraguay are neighboring South American countries that share extensive cultural, historical, and economic ties along their common border.
  • C. Uruguay
    Uruguay is a small South American country known for its stable democracy, high standard of living, and Atlantic coastline between Brazil and Argentina.
  • D. PARAGUAYA
    PARAGUAYA is the radio callsign used by LATAM Airlines Paraguay for air traffic control and communication purposes.
  • E. Bolivia
    Bolivia is a landlocked country in central South America known for its diverse indigenous cultures, Andean and Amazonian landscapes, and administrative capitals La Paz and Sucre.
  • 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_69c008a80f748190ba3d07ffc81acb29 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c05dc9f3d88190ab46b3b246f5a8df completed March 22, 2026, 9:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69c14173d2288190920b719e221a929c completed March 23, 2026, 1:34 p.m.
Created at: March 22, 2026, 4:18 p.m.