Triple

T4046947
Position Surface form Disambiguated ID Type / Status
Subject Faro E84088 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: [Faro, hasAirport, Faro Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Faro Airport
Context triple: [Faro, 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. Reykjavík Airport
    Reykjavík Airport is a domestic and regional airport located near the center of Iceland’s capital, serving as a key hub for internal flights and short-haul connections to nearby destinations.
  • C. Vágar Airport
    Vágar Airport is the main international airport serving the Faroe Islands, providing the primary air connection between the archipelago and other countries.
  • D. Leknes Airport
    Leknes Airport is a small regional airport in Norway’s Lofoten archipelago that provides vital domestic connections for residents and tourists.
  • E. Bardufoss Airport
    Bardufoss Airport is a regional airport in northern Norway that serves the town of Bardufoss and the surrounding Troms area, handling both civilian and military air traffic.
  • 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_69aed930bd5c819083e7dcc14fc44f69 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefb62593c8190ab8462c4d9cd9d08 completed March 9, 2026, 4:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5565466648190802a9b8fd88c3572 completed March 14, 2026, 12:36 p.m.
Created at: March 9, 2026, 3:37 p.m.