Triple

T11123142
Position Surface form Disambiguated ID Type / Status
Subject Quito School of Art E263066 entity
Predicate notableCenter P16715 FINISHED
Object San Francisco de Quito E8614 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: San Francisco de Quito | Statement: [Quito School of Art, notableCenter, San Francisco de Quito]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Francisco de Quito
Context triple: [Quito School of Art, notableCenter, San Francisco de Quito]
  • A. Quito chosen
    Quito is the high-altitude Andean city that serves as Ecuador’s political and cultural center, renowned for its well-preserved colonial historic center and dramatic mountain setting.
  • B. Cuenca
    Cuenca is a landlocked municipality in the province of Batangas in the Philippines, known for Mount Macolod and its agricultural communities.
  • C. Cuenca
    Cuenca is a historic city in southern Ecuador known for its well-preserved colonial architecture and cultural significance.
  • D. Cuenca
    Cuenca is a historic Spanish city renowned for its medieval architecture and dramatic “hanging houses” perched above deep river gorges.
  • E. Guayaquil
    Guayaquil is a major Pacific port city in southwestern Ecuador and the country’s principal commercial and industrial center.
  • 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_69d6aa9b46cc8190b19f9f0cc45bf322 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7e82e933481908550499cf9dd6531 completed April 9, 2026, 5:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69e509cd971c8190bdaa7b3c8a1f32f2 completed April 19, 2026, 4:58 p.m.
Created at: April 8, 2026, 9:28 p.m.