Triple

T26808751
Position Surface form Disambiguated ID Type / Status
Subject Frederikshavn Municipality E671920 entity
Predicate hasTouristAttraction P530 FINISHED
Object Skagen beaches
Skagen beaches are a renowned coastal area at Denmark’s northern tip, famous for their wide sandy shores, unique light that has inspired artists, and the meeting point of the North Sea and the Baltic Sea.
E1741830 NE FINISHED

How this triple was built (2 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: Skagen beaches | Statement: [Frederikshavn Municipality, hasTouristAttraction, Skagen beaches]
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: Skagen beaches
Triple: [Frederikshavn Municipality, hasTouristAttraction, Skagen beaches]
Generated description
Skagen beaches are a renowned coastal area at Denmark’s northern tip, famous for their wide sandy shores, unique light that has inspired artists, and the meeting point of the North Sea and the Baltic Sea.

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_69eeb3225a3c8190aaf6746efeded2f3 completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f61a20e4e88190a5ff15e9b7324c6a completed May 2, 2026, 3:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12097406c481908e90e515f568ba70 completed May 23, 2026, 8:09 p.m.
NEDg Description generation batch_6a120a8e2edc8190891be0f695a8c0e1 completed May 23, 2026, 8:14 p.m.
NED2 Entity disambiguation (via description) batch_6a120b9ce700819089a799bf42cbbac9 completed May 23, 2026, 8:18 p.m.
Created at: April 27, 2026, 4:28 a.m.