Triple

T10071945
Position Surface form Disambiguated ID Type / Status
Subject Rio Largo E213650 entity
Predicate locatedIn P40 FINISHED
Object Alagoas E26260 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: Alagoas | Statement: [Rio Largo, locatedIn, Alagoas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alagoas
Context triple: [Rio Largo, locatedIn, Alagoas]
  • A. Alagoas chosen
    Alagoas is a small coastal state in northeastern Brazil known for its picturesque beaches, lagoons, and colonial-era history.
  • B. Sergipe
    Sergipe is a small coastal state in northeastern Brazil known for its Atlantic shoreline, colonial history, and role in the broader Dutch and Portuguese colonial era.
  • C. Pernambuco
    Pernambuco is a northeastern Brazilian state known for its historic capital Recife, rich colonial and Afro-Brazilian cultural heritage, and significant role in Brazil’s sugarcane economy.
  • D. Paraíba
    Paraíba is a state in northeastern Brazil known for its Atlantic coastline, colonial history, and capital city João Pessoa.
  • E. Bahia
    Bahia is a traditional Brazilian football club based in Salvador, known for its passionate fanbase and historic success in national competitions.
  • 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_69ca839add308190b57d53b4ec21f2d0 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdd01279388190b94c8def00425c78 completed April 2, 2026, 2:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69d6a7bbfdb0819080377d3402bcfec3 completed April 8, 2026, 7:08 p.m.
Created at: March 30, 2026, 8:59 p.m.