Triple

T345672
Position Surface form Disambiguated ID Type / Status
Subject Fridtjof Nansen E6934 entity
Predicate placeOfDeath P21 FINISHED
Object Lysaker, Norway
Lysaker, Norway is a suburban area in Bærum just west of Oslo, known as a residential and commercial hub and historically associated with notable figures such as explorer Fridtjof Nansen.
 E43583 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: Lysaker, Norway | Statement: [Fridtjof Nansen, placeOfDeath, Lysaker, Norway]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lysaker, Norway
Context triple: [Fridtjof Nansen, placeOfDeath, Lysaker, Norway]
  • A. Lillehammer
    Lillehammer is a Norwegian town in the Gudbrandsdalen valley, best known internationally for staging the 1994 Winter Olympics.
  • B. Narvik
    Narvik is a port town in northern Norway known for its strategic importance during World War II and as the site of major naval and land battles.
  • C. Tøyen
    Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
  • D. Oslo
    Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
  • E. Gaustad
    Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
  • 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: Lysaker, Norway
Triple: [Fridtjof Nansen, placeOfDeath, Lysaker, Norway]
Generated description
Lysaker, Norway is a suburban area in Bærum just west of Oslo, known as a residential and commercial hub and historically associated with notable figures such as explorer Fridtjof Nansen.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lysaker, Norway
Target entity description: Lysaker, Norway is a suburban area in Bærum just west of Oslo, known as a residential and commercial hub and historically associated with notable figures such as explorer Fridtjof Nansen.
  • A. Lillehammer
    Lillehammer is a Norwegian town in the Gudbrandsdalen valley, best known internationally for staging the 1994 Winter Olympics.
  • B. Narvik
    Narvik is a port town in northern Norway known for its strategic importance during World War II and as the site of major naval and land battles.
  • C. Tøyen
    Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
  • D. Oslo
    Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
  • E. Gaustad
    Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
  • 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_69a2e7951ba08190960e90823b5078f3 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2eb0240e88190bc70784772f5fa30 completed Feb. 28, 2026, 1:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3d4ec430c8190abde193cadf3abc3 completed March 1, 2026, 5:55 a.m.
NEDg Description generation batch_69a3d57cfadc8190a828d4687a9e1a53 completed March 1, 2026, 5:58 a.m.
NED2 Entity disambiguation (via description) batch_69a3d602aa6881908fbdb4c25e8f8cb5 completed March 1, 2026, 6 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.