Triple

T27293
Position Surface form Disambiguated ID Type / Status
Subject OECD E546 entity
Predicate hasMember P10 FINISHED
Object Chile E203 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: Chile | Statement: [OECD, hasMember, Chile]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chile
Context triple: [OECD, hasMember, Chile]
  • A. Chile chosen
    Chile is a long, narrow South American country stretching along the Pacific coast, renowned for its diverse climates, stable economy, and world-class astronomical observatories.
  • B. Argentina
    Argentina is a large South American nation known for its diverse landscapes from the Andes to the Pampas, its vibrant culture including tango and football, and its capital city Buenos Aires.
  • C. Peru
    Peru is a South American country known for its rich Inca heritage, diverse landscapes from Andes mountains to Amazon rainforest, and the iconic archaeological site of Machu Picchu.
  • D. 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.
  • E. Colombia
    Colombia is a transcontinental country in northern South America, known for its diverse landscapes from Andes mountains to Amazon rainforest, rich cultural heritage, and major cities like Bogotá and Medellín.
  • 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_69a243b4ac2c8190b93c303df797b7b2 completed Feb. 28, 2026, 1:24 a.m.
NER Named-entity recognition batch_69a2467875048190aad87347c7a1cb67 completed Feb. 28, 2026, 1:36 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2ee84cbd08190a0eda3a148aea6c5 completed Feb. 28, 2026, 1:32 p.m.
Created at: Feb. 28, 2026, 1:34 a.m.