Triple

T11760936
Position Surface form Disambiguated ID Type / Status
Subject Intihuatana sector E279652 entity
Predicate locatedIn P40 FINISHED
Object Peru E2033 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: Peru | Statement: [Intihuatana sector, locatedIn, Peru]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peru
Context triple: [Intihuatana sector, locatedIn, Peru]
  • A. Peru chosen
    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.
  • B. Peru
    Peru is a small rural town in Berkshire County, Massachusetts, known for its elevated terrain and quiet, forested landscape in western New England.
  • C. Şile
    Şile is a coastal district on the Black Sea known for its beaches, lighthouse, and traditional Şile cloth, located on the Asian side of Istanbul, Turkey.
  • D. Ecuador
    Ecuador is a South American country on the Pacific coast, known for its diverse geography that includes part of the Amazon rainforest, the Andean highlands, and the Galápagos Islands.
  • E. Chile
    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.
  • 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_69d6ab01038c819080714901502c84fc completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a52386708190b744746a2db37495 completed April 10, 2026, 7:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69f1309f25a88190b0acaf7d9be6ae59 completed April 28, 2026, 10:11 p.m.
Created at: April 8, 2026, 9:41 p.m.