Triple

T6537540
Position Surface form Disambiguated ID Type / Status
Subject Ibura E168201 entity
Predicate hasState P35 FINISHED
Object Pernambuco E24892 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: Pernambuco | Statement: [Ibura, hasState, Pernambuco]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pernambuco
Context triple: [Ibura, hasState, Pernambuco]
  • A. Pernambuco chosen
    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.
  • 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. Ceará
    Ceará is a state in northeastern Brazil known for its long Atlantic coastline, semi-arid interior, and vibrant cultural traditions centered around its capital, Fortaleza.
  • D. Piauí
    Piauí is a state in northeastern Brazil known for its semi-arid landscapes, short Atlantic coastline, and rich archaeological sites such as those in Serra da Capivara National Park.
  • E. Alagoas
    Alagoas is a small coastal state in northeastern Brazil known for its picturesque beaches, lagoons, and colonial-era history.
  • 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_69c68a51564081909e93aee0dbd9cca3 completed March 27, 2026, 1:46 p.m.
NER Named-entity recognition batch_69c6add33acc8190bb0a9531648198f2 completed March 27, 2026, 4:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8be06206c81908e676d602234cd57 completed March 29, 2026, 5:52 a.m.
Created at: March 27, 2026, 1:49 p.m.