Triple

T3034247
Position Surface form Disambiguated ID Type / Status
Subject Faro Airport E82968 entity
Predicate countrySubdivision P766 FINISHED
Object Faro District E6080 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: Faro District | Statement: [Faro Airport, countrySubdivision, Faro District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Faro District
Context triple: [Faro Airport, countrySubdivision, Faro District]
  • A. Faro District chosen
    Faro District is the southernmost administrative district of mainland Portugal, encompassing much of the Algarve region and its popular coastal resorts.
  • B. Eysturoy
    Eysturoy is the second-largest island of the Faroe Islands, known for its rugged mountains, fjords, and traditional fishing villages.
  • C. Ofoten district
    Ofoten district is a traditional region in Nordland county in northern Norway, known for its fjords, mountains, and the town of Narvik as its main urban center.
  • D. Streymoy
    Streymoy is the largest and most populous island of the Faroe Islands, home to the capital city Tórshavn and the archipelago’s main political and economic activities.
  • E. Nordland
    Nordland is a long coastal county in northern Norway known for its dramatic fjords, islands like the Lofoten archipelago, and Arctic landscapes.
  • 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_69ad8b21a62881908ec5dd4fba4a187c completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9b2a40b48190bfa7cdbb0fbd87f8 completed March 8, 2026, 3:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1dec30d8081909d6ee691e5e51434 completed March 11, 2026, 9:29 p.m.
Created at: March 8, 2026, 3:01 p.m.