Triple

T579369
Position Surface form Disambiguated ID Type / Status
Subject Elfdalian E15024 entity
Predicate spokenIn P2266 FINISHED
Object Älvdalen
Älvdalen is a small municipality in central Sweden known for its forested landscapes, traditional culture, and preservation of the unique Elfdalian language.
 E72961 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: Älvdalen | Statement: [Elfdalian, spokenIn, Älvdalen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Älvdalen
Context triple: [Elfdalian, spokenIn, Älvdalen]
  • A. Gaustad
    Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
  • B. Tøyen
    Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
  • C. Strömstad
    Strömstad is a coastal town and municipality in western Sweden, near the Norwegian border, known for its archipelago, tourism, and ferry connections.
  • D. Lysgårdsbakken
    Lysgårdsbakken is a large ski jumping hill complex in Lillehammer, Norway, best known for hosting the ski jumping events of the 1994 Winter Olympics.
  • E. Östersund
    Östersund is a city in central Sweden known for its strong winter sports tradition and repeated bids to host the Winter Olympics.
  • 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: Älvdalen
Triple: [Elfdalian, spokenIn, Älvdalen]
Generated description
Älvdalen is a small municipality in central Sweden known for its forested landscapes, traditional culture, and preservation of the unique Elfdalian language.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Älvdalen
Target entity description: Älvdalen is a small municipality in central Sweden known for its forested landscapes, traditional culture, and preservation of the unique Elfdalian language.
  • A. Gaustad
    Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
  • B. Tøyen
    Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
  • C. Strömstad
    Strömstad is a coastal town and municipality in western Sweden, near the Norwegian border, known for its archipelago, tourism, and ferry connections.
  • D. Lysgårdsbakken
    Lysgårdsbakken is a large ski jumping hill complex in Lillehammer, Norway, best known for hosting the ski jumping events of the 1994 Winter Olympics.
  • E. Östersund
    Östersund is a city in central Sweden known for its strong winter sports tradition and repeated bids to host the Winter Olympics.
  • 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_69a4935783b8819082b77726ec10cc42 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49b6c358081908f458b9e3e208c0d completed March 1, 2026, 8:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69a508a025308190ac35a5e3606de4de completed March 2, 2026, 3:48 a.m.
NEDg Description generation batch_69a5090f5450819098324292a444fd94 completed March 2, 2026, 3:50 a.m.
NED2 Entity disambiguation (via description) batch_69a509facafc819089838e0724656ea0 completed March 2, 2026, 3:54 a.m.
Created at: March 1, 2026, 7:33 p.m.