Triple

T5943500
Position Surface form Disambiguated ID Type / Status
Subject Jelgava E132222 entity
Predicate hasTwinTown P919 FINISHED
Object Växjö E328762 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: Växjö | Statement: [Jelgava, hasTwinTown, Växjö]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Växjö
Context triple: [Jelgava, hasTwinTown, Växjö]
  • A. Växjö chosen
    Växjö is a city in southern Sweden known for its lakeside setting, environmental sustainability initiatives, and role as a regional cultural and educational center.
  • B. Skövde
    Skövde is a town in south-central Sweden that serves as a major military hub and training center for the Swedish Army.
  • C. Jönköping
    Jönköping is a city in southern Sweden, located at the southern end of Lake Vättern and known as a regional commercial and logistical hub.
  • D. Sundsvall
    Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
  • E. Örebro
    Örebro is a historic city in central Sweden known for its medieval castle, university, and role as a regional economic and cultural hub.
  • 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_69c00869d3308190af89b2453e0f7546 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c0393641d0819081c6c44816d94e4e completed March 22, 2026, 6:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69c77510f80c81908fe7784bdaa640c5 completed March 28, 2026, 6:28 a.m.
Created at: March 22, 2026, 4:01 p.m.