Triple

T2983630
Position Surface form Disambiguated ID Type / Status
Subject Hinnøya E80568 entity
Predicate hasMunicipality P847 FINISHED
Object Tjeldsund
Tjeldsund is a coastal municipality in northern Norway known for its location around the Tjeldsundet strait and its mix of island and mainland landscapes.
E330816 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: Tjeldsund | Statement: [Hinnøya, hasMunicipality, Tjeldsund]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tjeldsund
Context triple: [Hinnøya, hasMunicipality, Tjeldsund]
  • A. Bojnord
    Bojnord is a city in northeastern Iran that serves as the capital of North Khorasan Province.
  • B. Grimstad
    Grimstad is a coastal town and municipality in southern Norway known for its maritime heritage, charming wooden houses, and role as a summer tourist destination.
  • C. Svinesund
    Svinesund is a strait forming part of the border between Norway and Sweden, best known for its bridges and role as a major road crossing between the two countries.
  • D. Sandefjord
    Sandefjord is a coastal town and municipality in southern Norway known for its maritime heritage, whaling history, and popular seaside attractions.
  • E. Svolvær
    Svolvær is a coastal town in northern Norway that serves as a key fishing, tourism, and transport hub in the Lofoten archipelago.
  • 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: Tjeldsund
Triple: [Hinnøya, hasMunicipality, Tjeldsund]
Generated description
Tjeldsund is a coastal municipality in northern Norway known for its location around the Tjeldsundet strait and its mix of island and mainland landscapes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tjeldsund
Target entity description: Tjeldsund is a coastal municipality in northern Norway known for its location around the Tjeldsundet strait and its mix of island and mainland landscapes.
  • A. Bojnord
    Bojnord is a city in northeastern Iran that serves as the capital of North Khorasan Province.
  • B. Grimstad
    Grimstad is a coastal town and municipality in southern Norway known for its maritime heritage, charming wooden houses, and role as a summer tourist destination.
  • C. Svinesund
    Svinesund is a strait forming part of the border between Norway and Sweden, best known for its bridges and role as a major road crossing between the two countries.
  • D. Sandefjord
    Sandefjord is a coastal town and municipality in southern Norway known for its maritime heritage, whaling history, and popular seaside attractions.
  • E. Svolvær
    Svolvær is a coastal town in northern Norway that serves as a key fishing, tourism, and transport hub in the Lofoten archipelago.
  • 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_69ad8b15f6ac8190be5fd16a33edcb4f completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad99c481fc81909971c96352a881b4 completed March 8, 2026, 3:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b224ad6c30819086a9df7fc7c51ed8 completed March 12, 2026, 2:27 a.m.
NEDg Description generation batch_69b2254ac6d88190b40c7c5b5a0f4eba completed March 12, 2026, 2:30 a.m.
NED2 Entity disambiguation (via description) batch_69b225c6c2d88190adb381023e0c3219 completed March 12, 2026, 2:32 a.m.
Created at: March 8, 2026, 2:58 p.m.