Triple

T15723879
Position Surface form Disambiguated ID Type / Status
Subject Bissaya Barreto E381172 entity
Predicate workLocation P7 FINISHED
Object Coimbra region E1055522 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: Coimbra region | Statement: [Bissaya Barreto, workLocation, Coimbra region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Coimbra region
Context triple: [Bissaya Barreto, workLocation, Coimbra region]
  • A. Porto region
    The Porto region is a historic area in northern Portugal centered on the city of Porto, renowned for its cultural heritage, economic importance, and production of Port wine.
  • B. Região de Coimbra chosen
    Região de Coimbra is an administrative and statistical region in central Portugal that includes the historic city of Coimbra and surrounding municipalities.
  • C. Beira Baixa
    Beira Baixa is a historical region in central Portugal known for its rugged landscapes, traditional villages, and cultural heritage.
  • D. Beira Interior
    Beira Interior is a historical region in central Portugal known for its mountainous landscapes, fortified towns, and long-standing cultural and agricultural traditions.
  • E. Alentejo
    Alentejo is a large, sparsely populated region in southern Portugal known for its rolling plains, cork oak forests, vineyards, and historic whitewashed towns.
  • 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_69d86d9cdb648190bf3171be0bd7d872 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04fb1fdd4819088f3e243263e5f73 completed April 16, 2026, 2:55 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00d447f5cc81908757869f2d1e94a1 completed May 10, 2026, 6:54 p.m.
Created at: April 10, 2026, 4:46 a.m.