Triple

T4292800
Position Surface form Disambiguated ID Type / Status
Subject Corrientes E99634 entity
Predicate officialName P66 FINISHED
Object Ciudad de Corrientes E429749 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: Ciudad de Corrientes | Statement: [Corrientes, officialName, Ciudad de Corrientes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ciudad de Corrientes
Context triple: [Corrientes, officialName, Ciudad de Corrientes]
  • A. Ciudad de Corrientes chosen
    Ciudad de Corrientes is the capital city of the Corrientes Province in northeastern Argentina, known for its colonial architecture and location along the Paraná River.
  • B. Gualeguaychú
    Gualeguaychú is a city in eastern Argentina known for its vibrant Carnival celebrations and riverside tourism.
  • C. Tandil
    Tandil is a mid-sized city in central Argentina known for its scenic hilly landscapes, stone formations, and tourism-focused outdoor activities.
  • D. Catanduva
    Catanduva is a municipality in the northwestern region of the state of São Paulo, Brazil, known for its agricultural production and regional commercial importance.
  • E. Ciudad del Este
    Ciudad del Este is a major commercial city in eastern Paraguay, known as a busy border trading hub near the tri-border area with Brazil and Argentina.
  • 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_69b3455175088190aa79c6e03b86647e completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35082228081908504e3fd7c4ca1e8 completed March 12, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5d06fc60c8190a21fdfed689dac53 completed March 14, 2026, 9:17 p.m.
Created at: March 12, 2026, 11:08 p.m.