Triple

T3897836
Position Surface form Disambiguated ID Type / Status
Subject Mario Vargas Llosa E90413 entity
Predicate birthPlace P1 FINISHED
Object Arequipa, Peru E22142 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: Arequipa, Peru | Statement: [Mario Vargas Llosa, birthPlace, Arequipa, Peru]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arequipa, Peru
Context triple: [Mario Vargas Llosa, birthPlace, Arequipa, Peru]
  • A. Arequipa chosen
    Arequipa is Peru’s second-largest city, known for its colonial architecture built from white volcanic stone and its dramatic setting beneath the Misti volcano.
  • B. Juliaca
    Juliaca is a major commercial and transportation hub in southern Peru, known for its bustling markets and proximity to Lake Titicaca.
  • C. Cusco
    Cusco is a historic city in southeastern Peru that served as the capital of the Inca Empire and is now a major gateway to Machu Picchu.
  • D. Chimbote
    Chimbote is a coastal city in north-central Peru known for its fishing industry and port on the Pacific Ocean.
  • E. Pasco, Peru
    Pasco, Peru is a city in central Peru known for its high-altitude location in the Andes and its historical ties to mining and regional commerce.
  • 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_69aed95d315881908cbf1bf4a7215fbf completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeecd48b208190afaa62975805d087 completed March 9, 2026, 3:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69b53fe99ccc8190849ffe819a4bfd8f completed March 14, 2026, 11 a.m.
Created at: March 9, 2026, 3:21 p.m.