Triple

T4098451
Position Surface form Disambiguated ID Type / Status
Subject Helsinki Airport E87879 entity
Predicate locatedIn P40 FINISHED
Object Vantaa E395287 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: Vantaa | Statement: [Helsinki Airport, locatedIn, Vantaa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vantaa
Context triple: [Helsinki Airport, locatedIn, Vantaa]
  • A. Vantaa chosen
    Vantaa is a major city in the Helsinki metropolitan area of southern Finland, known for hosting Helsinki Airport and serving as an important commercial and residential hub.
  • B. Espoo
    Espoo is Finland’s second-largest city, located just west of Helsinki on the southern coast, known for its technology industry, natural landscapes, and role as part of the Helsinki metropolitan area.
  • C. Kirkkonummi
    Kirkkonummi is a municipality in southern Finland, located just west of Helsinki on the coast of the Gulf of Finland.
  • D. Järvenpää
    Järvenpää is a small city in southern Finland known for its lakeside setting and cultural heritage, including its association with composer Jean Sibelius.
  • E. Lahti
    Lahti is a city in southern Finland known for its winter sports facilities, particularly ski jumping and cross-country skiing, and for hosting numerous international sporting events.
  • 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_69aed94564cc8190a9c1457daedb6e7f completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefce017708190a4d33753fd32a7bb completed March 9, 2026, 5:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdb8f4eab88190b123aca20feb8d63 completed March 20, 2026, 9:15 p.m.
Created at: March 9, 2026, 3:40 p.m.