Triple

T995479
Position Surface form Disambiguated ID Type / Status
Subject Jules Laforgue E21485 entity
Predicate placeOfBirth P1 FINISHED
Object Montevideo E47651 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: Montevideo | Statement: [Jules Laforgue, placeOfBirth, Montevideo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Montevideo
Context triple: [Jules Laforgue, placeOfBirth, Montevideo]
  • A. Montevideo chosen
    Montevideo is the capital and largest city of Uruguay, serving as the country’s main political, economic, and cultural center.
  • B. Buenos Aires
    Buenos Aires is the capital and largest city of Argentina, known for its rich European-influenced culture, tango music and dance, and vibrant urban life.
  • C. Asunción
    Asunción is the capital and largest city of Paraguay, located along the Paraguay River and serving as the country’s main political, cultural, and economic center.
  • D. Colonia Buenos Aires
    Colonia Buenos Aires is a neighborhood located within the Cuauhtémoc borough in central Mexico City.
  • E. Asuncion
    Asuncion is a remote volcanic island in the Northern Mariana Islands, known for its steep stratovolcano and relatively undisturbed natural environment.
  • 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_69a493c476b48190b41fc5e793171cc6 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b4c75de88190bf7fec7a053f7a90 completed March 1, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac4c0bda208190b10b238a30466ea6 completed March 7, 2026, 4:02 p.m.
Created at: March 1, 2026, 7:41 p.m.