Triple

T2642473
Position Surface form Disambiguated ID Type / Status
Subject CP E62901 entity
Predicate connectsRegion P845 FINISHED
Object Alentejo 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 | Statement: [CP, connectsRegion, Alentejo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alentejo
Context triple: [CP, connectsRegion, Alentejo]
  • 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. Algarve
    Algarve is a popular coastal region in southern Portugal known for its beaches, cliffs, and resort 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_69ab4c3f2dcc819082df80f5e032f690 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abd8ff34988190ba9d69ce9d77c71d completed March 7, 2026, 7:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69b22496fda88190ba70356cc6835dcb completed March 12, 2026, 2:27 a.m.
Created at: March 6, 2026, 9:53 p.m.