Triple

T2912288
Position Surface form Disambiguated ID Type / Status
Subject Froland E63709 entity
Predicate hasSettlement P1068 FINISHED
Object Løddesøl
Løddesøl is a small village in Froland municipality in Agder county in southern Norway.
E308951 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: Løddesøl | Statement: [Froland, hasSettlement, Løddesøl]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Løddesøl
Context triple: [Froland, hasSettlement, Løddesøl]
  • A. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • B. Høvelte
    Høvelte is a locality in Denmark known primarily for hosting a major Danish Army military barracks and training area.
  • C. Mortensrud
    Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
  • D. Vadsø
    Vadsø is a small coastal town and administrative center in Finnmark, known for its Arctic location on the Varanger Peninsula and its role as a hub of Sami and Kven culture in Northern Norway.
  • E. Hokksund
    Hokksund is a small town in southeastern Norway known as the administrative center of Øvre Eiker municipality in Viken county.
  • 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: Løddesøl
Triple: [Froland, hasSettlement, Løddesøl]
Generated description
Løddesøl is a small village in Froland municipality in Agder county in southern Norway.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Løddesøl
Target entity description: Løddesøl is a small village in Froland municipality in Agder county in southern Norway.
  • A. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • B. Høvelte
    Høvelte is a locality in Denmark known primarily for hosting a major Danish Army military barracks and training area.
  • C. Mortensrud
    Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
  • D. Vadsø
    Vadsø is a small coastal town and administrative center in Finnmark, known for its Arctic location on the Varanger Peninsula and its role as a hub of Sami and Kven culture in Northern Norway.
  • E. Hokksund
    Hokksund is a small town in southeastern Norway known as the administrative center of Øvre Eiker municipality in Viken county.
  • 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_69ab4c44ab448190b9411324e8a1fc1d completed March 6, 2026, 9:51 p.m.
NER Named-entity recognition batch_69abe0eb77708190b745b887f3b9a618 completed March 7, 2026, 8:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69b0562014fc8190b7b702fa40682382 completed March 10, 2026, 5:34 p.m.
NEDg Description generation batch_69b05f7e78e8819095185f170ca26bda completed March 10, 2026, 6:14 p.m.
NED2 Entity disambiguation (via description) batch_69b0617a21a881909a0f52268a2494a6 completed March 10, 2026, 6:22 p.m.
Created at: March 6, 2026, 10:11 p.m.