Triple

T20711682
Position Surface form Disambiguated ID Type / Status
Subject Marinera Norteña E509063 entity
Predicate culturalRegion P1968 FINISHED
Object northern Peru NE NERFINISHED

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: northern Peru | Statement: [Marinera Norteña, culturalRegion, northern Peru]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: northern Peru
Context triple: [Marinera Norteña, culturalRegion, northern Peru]
  • A. northern Peru chosen
    Northern Peru is a geographic region of Peru known for its Andean highlands, rich pre-Columbian archaeological sites, and diverse coastal and jungle landscapes.
  • B. southeastern Peru
    Southeastern Peru is a region of the country that includes the historic Andean area around Cusco and extends toward the Amazon Basin.
  • C. western Peru
    Western Peru is the coastal and Andean region of Peru that includes major urban centers such as Lima and is characterized by arid Pacific shores and the western slopes of the Andes.
  • D. eastern Peru
    Eastern Peru is a remote, sparsely populated region dominated by Amazon rainforest, extensive river systems, and rich biodiversity.
  • E. Southern Peru
    Southern Peru is a geographic region of Peru known for its Andean highlands, volcanic landscapes, and major cities such as Arequipa and Cusco.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4c40ad88190b81f77695366d328 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c1cdcaac8190b9ba82d489fb3bfc completed April 21, 2026, 12:16 a.m.
Created at: April 16, 2026, 12:15 p.m.