Triple

T12313257
Position Surface form Disambiguated ID Type / Status
Subject São Paulo State University E293534 entity
Predicate hasCampusIn P4623 FINISHED
Object Assis E293516 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: Assis | Statement: [São Paulo State University, hasCampusIn, Assis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Assis
Context triple: [São Paulo State University, hasCampusIn, Assis]
  • A. Assis chosen
    Assis is a municipality in the western part of the state of São Paulo, Brazil, known as a regional commercial and educational center.
  • B. Azevêdo
    Azevêdo is a Portuguese-language surname commonly found in Brazil and Portugal, associated with several notable public figures.
  • C. Tadeu
    Tadeu is a given name, primarily used in Portuguese-speaking countries, that is a variant of the name Tadeusz.
  • D. Assin Foso
    Assin Foso is a prominent town in Ghana that serves as a commercial and administrative hub within the country’s Central Region.
  • E. Andrade
    Andrade is a common Portuguese and Spanish surname borne by numerous notable figures across fields such as sports, politics, and the arts.
  • 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_69d6ab6a2b50819082f6aedd32ed608a completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f03d3c88190baedffb83465bff8 completed April 10, 2026, 6:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69f61e86d45881909a9a3c09df0b78f1 completed May 2, 2026, 3:55 p.m.
Created at: April 8, 2026, 9:53 p.m.