Triple

T4960994
Position Surface form Disambiguated ID Type / Status
Subject Manuela Sáenz E111405 entity
Predicate residence P75 FINISHED
Object 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: Quito | Statement: [Manuela Sáenz, residence, Quito]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Quito
Context triple: [Manuela Sáenz, residence, 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. Guayaquil
    Guayaquil is a major Pacific port city in southwestern Ecuador and the country’s principal commercial and industrial center.
  • C. Bogotá
    Bogotá is the high-altitude capital and largest city of Colombia, known as a major political, economic, and cultural center in South America.
  • D. La Paz
    La Paz is the administrative capital and one of the major cities of Bolivia, known for its dramatic setting in a deep valley of the Andes at one of the highest elevations of any capital city in the world.
  • E. La Paz
    La Paz is the capital city of Baja California Sur in Mexico, known for its coastal location on the Gulf of California, marine biodiversity, and laid-back seaside atmosphere.
  • 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_69bd4419393c819086319a6fe4bf8542 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd71dc06a48190827d54a5c0351aab completed March 20, 2026, 4:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69be9244fb008190baee4ade5b00691f completed March 21, 2026, 12:42 p.m.
Created at: March 20, 2026, 1:32 p.m.