Triple

T5790642
Position Surface form Disambiguated ID Type / Status
Subject Gamle Oslo E128382 entity
Predicate containsNeighbourhood P4813 FINISHED
Object Teisen
Teisen is a residential neighborhood in Oslo, Norway, known for its apartment blocks, green spaces, and convenient access to public transportation.
 E547417 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: Teisen | Statement: [Gamle Oslo, containsNeighbourhood, Teisen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Teisen
Context triple: [Gamle Oslo, containsNeighbourhood, Teisen]
  • A. Isen
    Isen is a small town located on Tokunoshima in Japan’s Amami Islands, known for its subtropical climate and coastal scenery.
  • B. Thiesi
    Thiesi is a small town and comune in the Logudoro region of northern Sardinia, Italy, known for its agricultural traditions and pastoral landscape.
  • C. Tayshet
    Tayshet is a town in Irkutsk Oblast, Russia, known as a major railway junction in Siberia.
  • D. Terik
    Terik is a Southern Nilotic language spoken by the Terik people of western Kenya, closely related to Nandi and other Kalenjin languages.
  • E. Tirico
    Tirico is the surname of American sportscaster Mike Tirico, known for his play-by-play work on major sports broadcasts.
  • 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: Teisen
Triple: [Gamle Oslo, containsNeighbourhood, Teisen]
Generated description
Teisen is a residential neighborhood in Oslo, Norway, known for its apartment blocks, green spaces, and convenient access to public transportation.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Teisen
Target entity description: Teisen is a residential neighborhood in Oslo, Norway, known for its apartment blocks, green spaces, and convenient access to public transportation.
  • A. Isen
    Isen is a small town located on Tokunoshima in Japan’s Amami Islands, known for its subtropical climate and coastal scenery.
  • B. Thiesi
    Thiesi is a small town and comune in the Logudoro region of northern Sardinia, Italy, known for its agricultural traditions and pastoral landscape.
  • C. Tayshet
    Tayshet is a town in Irkutsk Oblast, Russia, known as a major railway junction in Siberia.
  • D. Terik
    Terik is a Southern Nilotic language spoken by the Terik people of western Kenya, closely related to Nandi and other Kalenjin languages.
  • E. Tirico
    Tirico is the surname of American sportscaster Mike Tirico, known for his play-by-play work on major sports broadcasts.
  • 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.