Triple

T20231028
Position Surface form Disambiguated ID Type / Status
Subject Lakeland Finland E495523 entity
Predicate majorCity P316 FINISHED
Object Tampere NE NERFINISHED

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: Tampere | Statement: [Lakeland Finland, majorCity, Tampere]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tampere
Context triple: [Lakeland Finland, majorCity, Tampere]
  • A. Tampere chosen
    Tampere is a major industrial and cultural city in southern Finland, historically significant as a key battleground in the Finnish Civil War.
  • B. Turku
    Turku is one of Finland’s oldest and historically most important cities, located on the southwest coast and known for its medieval heritage and major Baltic Sea port.
  • C. 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.
  • D. Lappeenranta
    Lappeenranta is a city in southeastern Finland near the Russian border, known for its lakeside location on Saimaa and its role as a regional commercial and educational center.
  • E. Kuopio
    Kuopio is a city in eastern Finland known for its lakeside setting, vibrant cultural life, and status as a regional center for education and commerce.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da626cff80819097b530718a7c98b6 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e66fdce9d081909752a6a1e15283ba completed April 20, 2026, 6:26 p.m.
Created at: April 11, 2026, 11:39 p.m.