Triple

T14249089
Position Surface form Disambiguated ID Type / Status
Subject State of Maranhão E353208 entity
Predicate colonialPower P160 FINISHED
Object Portugal E866 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: Portugal | Statement: [State of Maranhão, colonialPower, Portugal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Portugal
Context triple: [State of Maranhão, colonialPower, Portugal]
  • A. Portugal chosen
    Portugal is a Southern European country on the Iberian Peninsula, known for its maritime history, Atlantic coastline, and role as one of the world’s earliest global colonial powers.
  • B. Portogruaro
    Portogruaro is a historic town in northeastern Italy’s Veneto region, known for its medieval architecture and canals.
  • C. Portugal and Spain
    Portugal and Spain are neighboring Iberian countries in southwestern Europe known for their rich maritime histories, distinct Romance languages, and influential cultural and imperial legacies.
  • D. mainland Portugal
    Mainland Portugal is the continental part of the Portuguese Republic in southwestern Europe, comprising the country’s primary territory on the Iberian Peninsula.
  • E. Portela
    Portela is a residential parish in the municipality of Loures, within the Lisbon metropolitan area of Portugal.
  • 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_69d8278c43e08190824146f4632b89a5 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de6295ef9081909cfb0c1283bca21a completed April 14, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd3d1081148190b8830615a34711c0 completed May 8, 2026, 1:32 a.m.
Created at: April 10, 2026, 1:08 a.m.