Triple

T4046949
Position Surface form Disambiguated ID Type / Status
Subject Faro Airport E84088 entity
Predicate servesCity P82 FINISHED
Object Faro E84088 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 | Statement: [Faro Airport, servesCity, Faro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Faro
Context triple: [Faro Airport, servesCity, Faro]
  • A. Faro chosen
    Faro is a historic coastal city in southern Portugal that serves as the capital of the Algarve region and a major gateway for tourism.
  • B. Hafnarfjörður
    Hafnarfjörður is a port town in southwestern Iceland known for its lava-field setting, fishing industry, and role as part of the Reykjavík metropolitan area.
  • C. Dokkum
    Dokkum is a historic fortified town in the northern Netherlands, known as one of the Frisian Eleven Cities and for its association with the martyrdom of Saint Boniface.
  • D. Køpmannæhafn
    Køpmannæhafn is the historical Danish name for the city now known as Copenhagen, reflecting its origins as a merchant harbor.
  • E. Tórshavn
    Tórshavn is the capital and largest city of the Faroe Islands, serving as the political, cultural, and economic center of the archipelago.
  • 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.