Triple

T9639806
Position Surface form Disambiguated ID Type / Status
Subject Rusko E233032 entity
Predicate hasNeighbor P5707 FINISHED
Object Mynämäki E208429 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: Mynämäki | Statement: [Rusko, hasNeighbor, Mynämäki]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mynämäki
Context triple: [Rusko, hasNeighbor, Mynämäki]
  • A. Mynämäki chosen
    Mynämäki is a rural municipality in southwestern Finland known for its agricultural landscape and historic churches.
  • B. Kokemäki
    Kokemäki is a small town and municipality in the Satakunta region of western Finland, known for its location along the Kokemäenjoki River and its historical roots dating back to medieval times.
  • C. Tikkakoski
    Tikkakoski is a district in Jyväskylä, Finland, known for its military air base and role as a key center for the Finnish Air Force.
  • D. Luumäki
    Luumäki is a rural municipality in South Karelia, southeastern Finland, known for its forests, lakes, and historical significance.
  • E. Munkkiniemi
    Munkkiniemi is a residential district in western Helsinki, Finland, known for its seaside location, parks, and notable architecture.
  • 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_69ca848a5a908190aad251f4137b0c3a completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9b532aa4819087b56be6f5635126 completed April 1, 2026, 10:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69d18248ffe4819095d4ea20951eca01 completed April 4, 2026, 9:27 p.m.
Created at: March 30, 2026, 8:12 p.m.