Triple

T5790636
Position Surface form Disambiguated ID Type / Status
Subject Gamle Oslo E128382 entity
Predicate containsNeighbourhood P4813 FINISHED
Object Gamlebyen
Gamlebyen is the historic Old Town area of Oslo, known as the city's medieval core with archaeological sites, ruins, and preserved heritage buildings.
 E547413 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: Gamlebyen | Statement: [Gamle Oslo, containsNeighbourhood, Gamlebyen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gamlebyen
Context triple: [Gamle Oslo, containsNeighbourhood, Gamlebyen]
  • A. Gamlebyen (Old Town)
    Gamlebyen (Old Town) is the historic fortified quarter of Fredrikstad, Norway, renowned as one of the best-preserved fortified towns in Northern Europe.
  • B. Gudhjem
    Gudhjem is a picturesque coastal village on the Danish island of Bornholm, known for its steep streets, red-roofed houses, and harbor overlooking the Baltic Sea.
  • 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. Hellebæk
    Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
  • E. Blangsted
    Blangsted is a surname most notably associated with Folmar Blangsted, a film editor.
  • 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: Gamlebyen
Triple: [Gamle Oslo, containsNeighbourhood, Gamlebyen]
Generated description
Gamlebyen is the historic Old Town area of Oslo, known as the city's medieval core with archaeological sites, ruins, and preserved heritage buildings.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Gamlebyen
Target entity description: Gamlebyen is the historic Old Town area of Oslo, known as the city's medieval core with archaeological sites, ruins, and preserved heritage buildings.
  • A. Gamlebyen (Old Town)
    Gamlebyen (Old Town) is the historic fortified quarter of Fredrikstad, Norway, renowned as one of the best-preserved fortified towns in Northern Europe.
  • B. Gudhjem
    Gudhjem is a picturesque coastal village on the Danish island of Bornholm, known for its steep streets, red-roofed houses, and harbor overlooking the Baltic Sea.
  • 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. Hellebæk
    Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
  • E. Blangsted
    Blangsted is a surname most notably associated with Folmar Blangsted, a film editor.
  • 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.