Triple

T5790644
Position Surface form Disambiguated ID Type / Status
Subject Gamle Oslo E128382 entity
Predicate containsNeighbourhood P4813 FINISHED
Object Kværnerbyen
Kværnerbyen is a modern residential and commercial neighborhood in Oslo, Norway, developed on the former Kværner industrial site along the Alna River.
 E547419 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: Kværnerbyen | Statement: [Gamle Oslo, containsNeighbourhood, Kværnerbyen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kværnerbyen
Context triple: [Gamle Oslo, containsNeighbourhood, Kværnerbyen]
  • A. Kragerø
    Kragerø is a coastal town in Norway renowned for its picturesque archipelago, historic wooden buildings, and role as a popular summer holiday destination.
  • B. Kvænangen
    Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
  • C. Vårby
    Vårby is a suburban district in the southern Stockholm area of Sweden, known for its residential neighborhoods and proximity to Lake Mälaren.
  • D. Bremsnes
    Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
  • E. Kjøpsvik
    Kjøpsvik is a small village in Nordland county, Norway, situated along the Tysfjorden and known as a local industrial and ferry hub in the region.
  • 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: Kværnerbyen
Triple: [Gamle Oslo, containsNeighbourhood, Kværnerbyen]
Generated description
Kværnerbyen is a modern residential and commercial neighborhood in Oslo, Norway, developed on the former Kværner industrial site along the Alna River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kværnerbyen
Target entity description: Kværnerbyen is a modern residential and commercial neighborhood in Oslo, Norway, developed on the former Kværner industrial site along the Alna River.
  • A. Kragerø
    Kragerø is a coastal town in Norway renowned for its picturesque archipelago, historic wooden buildings, and role as a popular summer holiday destination.
  • B. Kvænangen
    Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
  • C. Vårby
    Vårby is a suburban district in the southern Stockholm area of Sweden, known for its residential neighborhoods and proximity to Lake Mälaren.
  • D. Bremsnes
    Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
  • E. Kjøpsvik
    Kjøpsvik is a small village in Nordland county, Norway, situated along the Tysfjorden and known as a local industrial and ferry hub in the region.
  • 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_69c00845ca68819081a2ce3ecca577f7 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02a5585788190821b8da40259e0e7 completed March 22, 2026, 5:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69c09820f5c08190811e848eb44ce5b9 completed March 23, 2026, 1:32 a.m.
NEDg Description generation batch_69c0990bf38081908c09c5dfe660c35b completed March 23, 2026, 1:36 a.m.
NED2 Entity disambiguation (via description) batch_69c099b4bc4481909e7cf6886e5ccbea completed March 23, 2026, 1:39 a.m.
Created at: March 22, 2026, 3:51 p.m.