Triple

T8436370
Position Surface form Disambiguated ID Type / Status
Subject Bahia E199233 entity
Predicate borders P224 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: [Bahia, borders, Alagoas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alagoas
Context triple: [Bahia, borders, 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_69ca8314cd6c8190a6b8c2a1096e18f3 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe132a6f881908f990089792fccc4 completed March 31, 2026, 2:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69d10fff477481908c95f371d03b2189 completed April 4, 2026, 1:19 p.m.
Created at: March 30, 2026, 6:08 p.m.