Triple

T8661246
Position Surface form Disambiguated ID Type / Status
Subject Évora E205552 entity
Predicate partOf P40 FINISHED
Object Alentejo Region E38350 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: Alentejo Region | Statement: [Évora, partOf, Alentejo Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alentejo Region
Context triple: [Évora, partOf, Alentejo Region]
  • A. Alentejo chosen
    Alentejo is a large, sparsely populated region in southern Portugal known for its rolling plains, cork oak forests, vineyards, and historic whitewashed towns.
  • B. Alto Alentejo
    Alto Alentejo is a subregion in northern Alentejo, Portugal, known for its historic towns, rural landscapes, and traditional agriculture.
  • C. Alentejo Central
    Alentejo Central is a subregion in southern Portugal known for its historic towns, rolling plains, and wine production within the broader Alentejo region.
  • D. Alentejo Litoral
    Alentejo Litoral is a coastal subregion of Portugal’s Alentejo known for its Atlantic beaches, rural landscapes, and traditional agriculture.
  • E. Baixo Alentejo
    Baixo Alentejo is a sparsely populated, predominantly rural subregion in southern Portugal known for its rolling plains, cork oak forests, and traditional agriculture.
  • 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_69ca8350897c819086cde7596fbe5fe7 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc487147248190b5f1bff836a11e68 completed March 31, 2026, 10:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69d121cb33188190b5b70020a041c18f completed April 4, 2026, 2:35 p.m.
Created at: March 30, 2026, 6:30 p.m.