Triple

T5235080
Position Surface form Disambiguated ID Type / Status
Subject Harriet Monroe E118201 entity
Predicate placeOfDeath P21 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: [Harriet Monroe, placeOfDeath, Arequipa, Peru]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arequipa, Peru
Context triple: [Harriet Monroe, placeOfDeath, 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_69bd4467db0881909b3b0982df32cc8f completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd7b064b6881909f5746f55aa422c6 completed March 20, 2026, 4:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69bef81cca948190ab00302787367f43 completed March 21, 2026, 7:57 p.m.
Created at: March 20, 2026, 1:49 p.m.