Triple

T2795699
Position Surface form Disambiguated ID Type / Status
Subject Fredrikstad E53030 entity
Predicate borderedBy P224 FINISHED
Object Hvaler
Hvaler is a Norwegian coastal municipality and archipelago in Viken county, known for its islands, fishing communities, and popular seaside recreation areas.
 E298659 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: Hvaler | Statement: [Fredrikstad, borderedBy, Hvaler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hvaler
Context triple: [Fredrikstad, borderedBy, Hvaler]
  • A. Orcines
    Orcines is a commune in central France’s Puy-de-Dôme department, known for its proximity to the Chaîne des Puys volcanic range.
  • B. Gadus
    Gadus is a genus of marine fish that includes the economically important true cods found in cold and temperate waters of the Northern Hemisphere.
  • C. Dolphin
    Dolphin was the internal codename used by Nintendo during the development of the GameCube console.
  • D. Beluga
    The Beluga is a large, bulbous-headed cargo aircraft developed by Airbus, known for transporting oversized aerospace components.
  • E. Manta
    Manta is a major coastal city and important seaport in western Ecuador, known for its fishing industry, beaches, and commercial activity.
  • 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: Hvaler
Triple: [Fredrikstad, borderedBy, Hvaler]
Generated description
Hvaler is a Norwegian coastal municipality and archipelago in Viken county, known for its islands, fishing communities, and popular seaside recreation areas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hvaler
Target entity description: Hvaler is a Norwegian coastal municipality and archipelago in Viken county, known for its islands, fishing communities, and popular seaside recreation areas.
  • A. Orcines
    Orcines is a commune in central France’s Puy-de-Dôme department, known for its proximity to the Chaîne des Puys volcanic range.
  • B. Gadus
    Gadus is a genus of marine fish that includes the economically important true cods found in cold and temperate waters of the Northern Hemisphere.
  • C. Dolphin
    Dolphin was the internal codename used by Nintendo during the development of the GameCube console.
  • D. Beluga
    The Beluga is a large, bulbous-headed cargo aircraft developed by Airbus, known for transporting oversized aerospace components.
  • E. Manta
    Manta is a major coastal city and important seaport in western Ecuador, known for its fishing industry, beaches, and commercial activity.
  • 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_69ab495a90788190941b6917e1eca3a6 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abddef754081908e6218dc2208e0fd completed March 7, 2026, 8:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc6646c2c81908157d8f03cb8376d completed March 10, 2026, 7:21 a.m.
NEDg Description generation batch_69afc70a4e008190a846d23e1aa73bb1 completed March 10, 2026, 7:23 a.m.
NED2 Entity disambiguation (via description) batch_69afc7907be88190b70458ed735261e8 completed March 10, 2026, 7:26 a.m.
Created at: March 6, 2026, 9:58 p.m.