Triple

T10229713
Position Surface form Disambiguated ID Type / Status
Subject Kronoberg County E243306 entity
Predicate contains P35 FINISHED
Object Alvesta
Alvesta is a locality and railway junction in southern Sweden that serves as the seat of Alvesta Municipality in Kronoberg County.
E851315 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: Alvesta | Statement: [Kronoberg County, contains, Alvesta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alvesta
Context triple: [Kronoberg County, contains, Alvesta]
  • A. Garmsar
    Garmsar is a city in Semnan Province of north-central Iran, known as a regional transport hub and gateway between Tehran and eastern parts of the country.
  • B. Orbost
    Orbost is a small rural town in East Gippsland, Victoria, Australia, known as a service centre for the surrounding farming and forestry region near the Snowy River.
  • C. Kingissepa
    Kingissepa is the former Soviet-era name of the Estonian town now known as Kuressaare, located on Saaremaa Island.
  • D. Mora
    Mora is a canton in Costa Rica’s San José Province known for its rural landscapes, agricultural activities, and small-town communities.
  • E. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • 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: Alvesta
Triple: [Kronoberg County, contains, Alvesta]
Generated description
Alvesta is a locality and railway junction in southern Sweden that serves as the seat of Alvesta Municipality in Kronoberg County.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Alvesta
Target entity description: Alvesta is a locality and railway junction in southern Sweden that serves as the seat of Alvesta Municipality in Kronoberg County.
  • A. Garmsar
    Garmsar is a city in Semnan Province of north-central Iran, known as a regional transport hub and gateway between Tehran and eastern parts of the country.
  • B. Orbost
    Orbost is a small rural town in East Gippsland, Victoria, Australia, known as a service centre for the surrounding farming and forestry region near the Snowy River.
  • C. Kingissepa
    Kingissepa is the former Soviet-era name of the Estonian town now known as Kuressaare, located on Saaremaa Island.
  • D. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • E. Mora
    Mora is a canton in Costa Rica’s San José Province known for its rural landscapes, agricultural activities, and small-town communities.
  • 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_69d381b0f97c819085c9b45799a5fb7c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d1fcb1d081908173033594a6bfc9 completed April 7, 2026, 9:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69d6f73610fc8190965c4e45a9deeac6 completed April 9, 2026, 12:47 a.m.
NEDg Description generation batch_69d6fa2ea97081908395048218c0592b completed April 9, 2026, 1 a.m.
NED2 Entity disambiguation (via description) batch_69d6fcb5dc4c8190944a423a9d16a4b8 completed April 9, 2026, 1:11 a.m.
Created at: April 6, 2026, 11:19 a.m.