Triple

T11403270
Position Surface form Disambiguated ID Type / Status
Subject Azuay Province E270169 entity
Predicate contains P35 FINISHED
Object Cuenca E133952 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: Cuenca | Statement: [Azuay Province, contains, Cuenca]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cuenca
Context triple: [Azuay Province, contains, Cuenca]
  • A. Cuenca
    Cuenca is a historic Spanish city renowned for its medieval architecture and dramatic “hanging houses” perched above deep river gorges.
  • B. Cuenca chosen
    Cuenca is a historic city in southern Ecuador known for its well-preserved colonial architecture and cultural significance.
  • C. Cuenca
    Cuenca is a landlocked municipality in the province of Batangas in the Philippines, known for Mount Macolod and its agricultural communities.
  • D. Calarcá
    Calarcá is a Colombian town and municipality in the coffee-growing Quindío Department, known for its cultural heritage and role in the Coffee Cultural Landscape.
  • E. Suesca
    Suesca is a Colombian town in the department of Cundinamarca, renowned for its dramatic rock cliffs that make it a popular destination for rock climbing and outdoor recreation.
  • 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_69d6aaddeaa8819088b30ef7b50598c9 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8014ab46881909fa1d425926c617b completed April 9, 2026, 7:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5b834cee48190ac09c1e1df4c12d0 completed April 20, 2026, 5:23 a.m.
Created at: April 8, 2026, 9:34 p.m.