Triple

T295332
Position Surface form Disambiguated ID Type / Status
Subject Algarve E6079 entity
Predicate hasAirport P105 FINISHED
Object Faro Airport E11788 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 Airport | Statement: [Algarve, hasAirport, Faro Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Faro Airport
Context triple: [Algarve, hasAirport, Faro Airport]
  • A. Faro Airport chosen
    Faro Airport is the main international airport serving Portugal’s Algarve region, handling millions of tourists each year who visit its popular coastal resorts.
  • B. Isle of Man Airport
    Isle of Man Airport is the primary commercial air hub serving the Isle of Man, providing regional and limited international flights to and from the island.
  • C. Oslo Airport, Gardermoen
    Oslo Airport, Gardermoen is Norway’s main international airport and the primary aviation hub serving the Oslo region.
  • D. Stockholm Bromma Airport
    Stockholm Bromma Airport is a regional airport near central Stockholm, Sweden, primarily serving domestic and short-haul European flights.
  • E. Tenerife North Airport
    Tenerife North Airport is a major passenger airport on the island of Tenerife in Spain’s Canary Islands, serving primarily domestic and inter-island flights.
  • 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_69a2e79114b081909490b3bf5a5dbb51 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2e979663481908cf9622e59fed041 completed Feb. 28, 2026, 1:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3b071c6e881908e9ca17bd5dc911e completed March 1, 2026, 3:20 a.m.
Created at: Feb. 28, 2026, 1:06 p.m.