Triple

T5689380
Position Surface form Disambiguated ID Type / Status
Subject Brasília Time E125390 entity
Predicate observedIn P40 FINISHED
Object Aracaju E166976 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: Aracaju | Statement: [Brasília Time, observedIn, Aracaju]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aracaju
Context triple: [Brasília Time, observedIn, Aracaju]
  • A. Aracaju chosen
    Aracaju is a coastal city in northeastern Brazil known for its planned urban layout, beaches, and role as an administrative and economic center.
  • B. Maceió
    Maceió is a coastal city in northeastern Brazil known for its white-sand beaches, turquoise waters, and vibrant tourism industry.
  • C. Recife
    Recife is a major coastal city in northeastern Brazil known for its historic colonial architecture, extensive waterways, and role as an important cultural and economic center.
  • D. Jaboatão dos Guararapes
    Jaboatão dos Guararapes is a major coastal city in northeastern Brazil known for its historical significance in the Dutch-Portuguese conflicts and its integration into the metropolitan area of Recife.
  • E. Teresina
    Teresina is the capital and largest city of the Brazilian state of Piauí, known for its hot climate and location near the confluence of the Parnaíba and Poti rivers.
  • 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_69c0082bb19c8190823a4facd3cba79b completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c023e1c6148190aeae7620bd9ee9d4 completed March 22, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c51892bb648190987b9626ef7ea4d9 completed March 26, 2026, 11:29 a.m.
Created at: March 22, 2026, 3:44 p.m.