Triple

T7398511
Position Surface form Disambiguated ID Type / Status
Subject North Sea coast of Denmark E170685 entity
Predicate contains P35 FINISHED
Object Løkken
Løkken is a Danish seaside town known for its sandy beaches, coastal dunes, and popular summer tourism on the North Sea.
 E661447 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: Løkken | Statement: [North Sea coast of Denmark, contains, Løkken]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Løkken
Context triple: [North Sea coast of Denmark, contains, Løkken]
  • A. Birkelunden
    Birkelunden is a popular public park in Oslo’s Grünerløkka district, known for its green spaces, cultural events, and historic surroundings.
  • B. Rønne
    Rønne is the largest town and administrative center of the Danish island of Bornholm, known for its historic harbor, half-timbered houses, and Baltic Sea ferry connections.
  • C. Kvænangen
    Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
  • D. Lodalen
    Lodalen is a small valley and residential-industrial area in Oslo, Norway, situated near the inner-city districts and railway facilities.
  • E. Lødingen
    Lødingen is a coastal municipality in Nordland county, Norway, located on the island of Hinnøya and known for its fishing, maritime activities, and scenic fjord landscape.
  • 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: Løkken
Triple: [North Sea coast of Denmark, contains, Løkken]
Generated description
Løkken is a Danish seaside town known for its sandy beaches, coastal dunes, and popular summer tourism on the North Sea.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Løkken
Target entity description: Løkken is a Danish seaside town known for its sandy beaches, coastal dunes, and popular summer tourism on the North Sea.
  • A. Birkelunden
    Birkelunden is a popular public park in Oslo’s Grünerløkka district, known for its green spaces, cultural events, and historic surroundings.
  • B. Rønne
    Rønne is the largest town and administrative center of the Danish island of Bornholm, known for its historic harbor, half-timbered houses, and Baltic Sea ferry connections.
  • C. Kvænangen
    Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
  • D. Lodalen
    Lodalen is a small valley and residential-industrial area in Oslo, Norway, situated near the inner-city districts and railway facilities.
  • E. Lødingen
    Lødingen is a coastal municipality in Nordland county, Norway, located on the island of Hinnøya and known for its fishing, maritime activities, and scenic fjord landscape.
  • 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_69c68a5f04188190ac266569c9280347 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f24c1c208190a3d11e816888760d completed March 27, 2026, 9:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69c81106c0788190a3740acf7bb4ab86 completed March 28, 2026, 5:33 p.m.
NEDg Description generation batch_69c811e0ebec8190b394b1a2ff6ac5bf completed March 28, 2026, 5:37 p.m.
NED2 Entity disambiguation (via description) batch_69c8127599188190af3d049a0c6dd349 completed March 28, 2026, 5:40 p.m.
Created at: March 27, 2026, 3:09 p.m.