Triple

T13512720
Position Surface form Disambiguated ID Type / Status
Subject Valtion Lentokonetehdas E322677 entity
Predicate designedAircraft P31176 FINISHED
Object VL Sääski
VL Sääski was a Finnish two-seat trainer and liaison aircraft from the interwar period, developed for the Finnish Air Force.
E1044947 NE FINISHED

How this triple was built (4 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: VL Sääski | Statement: [Valtion Lentokonetehdas, designedAircraft, VL Sääski]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: VL Sääski
Context triple: [Valtion Lentokonetehdas, designedAircraft, VL Sääski]
  • A. VL Viima
    The VL Viima is a Finnish two-seat biplane trainer aircraft used primarily for pilot training in the 1930s and 1940s.
  • B. Virtanen
    Virtanen is a common Finnish surname borne by numerous notable individuals across fields such as science, sports, and the arts.
  • C. Vaino
    Vaino is a given name, likely a variant or transliteration of the Finnish name Väinö, used as a personal first name.
  • D. Ilmari
    Ilmari is a Finnish given name, notably borne by Nobel Prize–winning biochemist A. I. Virtanen.
  • E. Veikkola
    Veikkola is a village in southern Finland that forms part of the municipality of Kirkkonummi in the Uusimaa region.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: VL Sääski
Triple: [Valtion Lentokonetehdas, designedAircraft, VL Sääski]
Generated description
VL Sääski was a Finnish two-seat trainer and liaison aircraft from the interwar period, developed for the Finnish Air Force.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: VL Sääski
Target entity description: VL Sääski was a Finnish two-seat trainer and liaison aircraft from the interwar period, developed for the Finnish Air Force.
  • A. VL Viima
    The VL Viima is a Finnish two-seat biplane trainer aircraft used primarily for pilot training in the 1930s and 1940s.
  • B. Virtanen
    Virtanen is a common Finnish surname borne by numerous notable individuals across fields such as science, sports, and the arts.
  • C. Vaino
    Vaino is a given name, likely a variant or transliteration of the Finnish name Väinö, used as a personal first name.
  • D. Ilmari
    Ilmari is a Finnish given name, notably borne by Nobel Prize–winning biochemist A. I. Virtanen.
  • E. Veikkola
    Veikkola is a village in southern Finland that forms part of the municipality of Kirkkonummi in the Uusimaa region.
  • F. None of above. chosen

Provenance (5 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_69d80766a21881909f21a1b7421d3b8a completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbaf87ca288190a147fbdb2f90985f completed April 12, 2026, 2:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75492676c81909602745e2b6436cb completed May 3, 2026, 1:58 p.m.
NEDg Description generation batch_69f7555173d08190be887e81c148192e completed May 3, 2026, 2:01 p.m.
NED2 Entity disambiguation (via description) batch_69f756773b9c81908250ae7ffc2d8d99 completed May 3, 2026, 2:06 p.m.
Created at: April 9, 2026, 9:44 p.m.