Triple

T257866
Position Surface form Disambiguated ID Type / Status
Subject Denmark E5474 entity
Predicate hasTerritory P285 FINISHED
Object Funen
Funen is Denmark’s third-largest island, located between the Jutland Peninsula and Zealand and known for its rolling countryside and the city of Odense, birthplace of Hans Christian Andersen.
 E33638 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: Funen | Statement: [Denmark, hasTerritory, Funen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Funen
Context triple: [Denmark, hasTerritory, Funen]
  • A. Bornholm
    Bornholm is a Danish island known for its rocky coastline, medieval ruins, and picturesque fishing villages in the Baltic Sea.
  • B. Öland
    Öland is Sweden’s second-largest island, known for its unique limestone plains, rich birdlife, and popular summer tourism along the Baltic Sea coast.
  • C. Gotland
    Gotland is Sweden’s largest island, located in the Baltic Sea and known for its medieval town of Visby, limestone cliffs, and rich Viking-era history.
  • D. Rügen
    Rügen is Germany’s largest island, known for its chalk cliffs, seaside resorts, and beaches along the Baltic Sea coast.
  • E. Arendal
    Arendal is a coastal town and municipality in southern Norway known historically as a regional political and trading center.
  • 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: Funen
Triple: [Denmark, hasTerritory, Funen]
Generated description
Funen is Denmark’s third-largest island, located between the Jutland Peninsula and Zealand and known for its rolling countryside and the city of Odense, birthplace of Hans Christian Andersen.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Funen
Target entity description: Funen is Denmark’s third-largest island, located between the Jutland Peninsula and Zealand and known for its rolling countryside and the city of Odense, birthplace of Hans Christian Andersen.
  • A. Bornholm
    Bornholm is a Danish island known for its rocky coastline, medieval ruins, and picturesque fishing villages in the Baltic Sea.
  • B. Öland
    Öland is Sweden’s second-largest island, known for its unique limestone plains, rich birdlife, and popular summer tourism along the Baltic Sea coast.
  • C. Gotland
    Gotland is Sweden’s largest island, located in the Baltic Sea and known for its medieval town of Visby, limestone cliffs, and rich Viking-era history.
  • D. Rügen
    Rügen is Germany’s largest island, known for its chalk cliffs, seaside resorts, and beaches along the Baltic Sea coast.
  • E. Arendal
    Arendal is a coastal town and municipality in southern Norway known historically as a regional political and trading center.
  • 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_69a2580a64ac8190ad76e34bb0715b5e completed Feb. 28, 2026, 2:50 a.m.
NER Named-entity recognition batch_69a25d703e688190bb86c69527e306f5 completed Feb. 28, 2026, 3:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3837479408190bb6e6f0eb6a7fe46 completed March 1, 2026, 12:08 a.m.
NEDg Description generation batch_69a384140aec8190ab918512cf088464 completed March 1, 2026, 12:11 a.m.
NED2 Entity disambiguation (via description) batch_69a3848c0ed08190acf0d0e33ca8b41c completed March 1, 2026, 12:13 a.m.
Created at: Feb. 28, 2026, 2:55 a.m.