Triple

T21376788
Position Surface form Disambiguated ID Type / Status
Subject Vila Franca de Xira E527228 entity
Predicate locatedInHistoricalProvince P5057 FINISHED
Object Ribatejo NE NERFINISHED

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: Ribatejo | Statement: [Vila Franca de Xira, locatedInHistoricalProvince, Ribatejo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ribatejo
Context triple: [Vila Franca de Xira, locatedInHistoricalProvince, Ribatejo]
  • A. Ribatejo chosen
    Ribatejo is a historical province in central Portugal known for its fertile plains, agriculture, and traditional bullfighting culture.
  • B. Potrerillos
    Potrerillos is a town and municipality located in the Cortés Department of northwestern Honduras.
  • C. Riparbella
    Riparbella is a small Tuscan hill town in central Italy, known for its rural landscapes, vineyards, and traditional agricultural economy.
  • D. Turrubares
    Turrubares is a rural canton in Costa Rica known for its mountainous landscapes, agricultural activities, and low population density.
  • E. Negrete
    Negrete is a small town and commune in Chile’s Biobío Region, known for its rural character and location near the Biobío River.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b51f363c8190944000ab5523b02b completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e8b0c79df88190b4b9804efebc8c6e completed April 22, 2026, 11:28 a.m.
Created at: April 16, 2026, 5:11 p.m.