Triple

T17579667
Position Surface form Disambiguated ID Type / Status
Subject Ecclesiastical province of Chimbote E428166 entity
Predicate hasCathedralCity P9022 FINISHED
Object Chimbote 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: Chimbote | Statement: [Ecclesiastical province of Chimbote, hasCathedralCity, Chimbote]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chimbote
Context triple: [Ecclesiastical province of Chimbote, hasCathedralCity, Chimbote]
  • A. Chimbote chosen
    Chimbote is a coastal city in north-central Peru known for its fishing industry and port on the Pacific Ocean.
  • B. Arequipa
    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.
  • C. Chivay
    Chivay is a small Andean town in southern Peru that serves as the main gateway and service hub for visitors to the Colca Canyon.
  • D. Talara
    Talara is a coastal city in northwestern Peru known for its important oil industry and nearby Pacific beaches.
  • E. Juliaca
    Juliaca is a major commercial and transportation hub in southern Peru, known for its bustling markets and proximity to Lake Titicaca.
  • 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_69d889e1030481909950e140c63255b9 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e463cc493c8190965680cf786aa531 completed April 19, 2026, 5:10 a.m.
Created at: April 10, 2026, 5:50 a.m.