Triple

T1047350
Position Surface form Disambiguated ID Type / Status
Subject Lyn Fotball E22611 entity
Predicate shortName P43 FINISHED
Object Lyn
Lyn is a Norwegian football club based in Oslo with a long history in the country’s top divisions.
 E124540 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: Lyn | Statement: [Lyn Fotball, shortName, Lyn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lyn
Context triple: [Lyn Fotball, shortName, Lyn]
  • A. Lynn
    Lynn is a coastal city in northeastern Massachusetts, known as one of the larger urban centers in the Greater Boston metropolitan area.
  • B. Lys
    The Lys is a river in northern France and western Belgium that flows through cities like Ghent and is known for its historical role in trade and the textile industry.
  • C. Lina
    Lina is a Native American servant in Toni Morrison’s novel *A Mercy*, whose history of displacement and resilience reflects the novel’s themes of slavery, colonialism, and survival in 17th-century America.
  • D. Lyness
    Lyness is a small coastal village and former naval base on the island of Hoy in Orkney, Scotland.
  • E. Linda
    Linda is a feminine given name of Germanic origin that became widely used in English-speaking countries in the 20th century.
  • 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: Lyn
Triple: [Lyn Fotball, shortName, Lyn]
Generated description
Lyn is a Norwegian football club based in Oslo with a long history in the country’s top divisions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lyn
Target entity description: Lyn is a Norwegian football club based in Oslo with a long history in the country’s top divisions.
  • A. Lynn
    Lynn is a coastal city in northeastern Massachusetts, known as one of the larger urban centers in the Greater Boston metropolitan area.
  • B. Lys
    The Lys is a river in northern France and western Belgium that flows through cities like Ghent and is known for its historical role in trade and the textile industry.
  • C. Lina
    Lina is a Native American servant in Toni Morrison’s novel *A Mercy*, whose history of displacement and resilience reflects the novel’s themes of slavery, colonialism, and survival in 17th-century America.
  • D. Lyness
    Lyness is a small coastal village and former naval base on the island of Hoy in Orkney, Scotland.
  • E. Linda
    Linda is a feminine given name of Germanic origin that became widely used in English-speaking countries in the 20th century.
  • 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_69a493da02e081908c13ff5e02a0fe7a completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b84d30888190b66f7245d781957d completed March 1, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac429cc3c481909c55459790d6857f completed March 7, 2026, 3:22 p.m.
NEDg Description generation batch_69ac4394156881909042176e035414be completed March 7, 2026, 3:26 p.m.
NED2 Entity disambiguation (via description) batch_69ac43ded4308190bbedda3e2c6255a4 completed March 7, 2026, 3:27 p.m.
Created at: March 1, 2026, 7:42 p.m.