Triple

T22237956
Position Surface form Disambiguated ID Type / Status
Subject Gribskov Municipality E549640 entity
Predicate contains P35 FINISHED
Object Rågeleje
Rågeleje is a coastal village and seaside resort in northern Zealand, Denmark, known for its beach, summer houses, and scenic views over the Kattegat.
 E1526091 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: Rågeleje | Statement: [Gribskov Municipality, contains, Rågeleje]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rågeleje
Context triple: [Gribskov Municipality, contains, Rågeleje]
  • A. Rielasingen
    Rielasingen is a locality within the municipality of Rielasingen-Worblingen in the district of Konstanz in Baden-Württemberg, Germany.
  • B. Hyra
    Hyra is the surname of American actress and producer Meg Ryan, known for her roles in popular romantic comedies of the late 20th century.
  • C. Råket
    Råket is a small island that forms part of the Smøla archipelago in Møre og Romsdal county, Norway.
  • D. Rælingen
    Rælingen is a municipality in Viken county, Norway, known for its proximity to Oslo and its mix of residential areas, forests, and lakes.
  • E. Pilestredet
    Pilestredet is a central street in Oslo, Norway, known for hosting parts of Oslo Metropolitan University's main campus and various urban institutions.
  • 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: Rågeleje
Triple: [Gribskov Municipality, contains, Rågeleje]
Generated description
Rågeleje is a coastal village and seaside resort in northern Zealand, Denmark, known for its beach, summer houses, and scenic views over the Kattegat.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rågeleje
Target entity description: Rågeleje is a coastal village and seaside resort in northern Zealand, Denmark, known for its beach, summer houses, and scenic views over the Kattegat.
  • A. Rielasingen
    Rielasingen is a locality within the municipality of Rielasingen-Worblingen in the district of Konstanz in Baden-Württemberg, Germany.
  • B. Hyra
    Hyra is the surname of American actress and producer Meg Ryan, known for her roles in popular romantic comedies of the late 20th century.
  • C. Råket
    Råket is a small island that forms part of the Smøla archipelago in Møre og Romsdal county, Norway.
  • D. Rælingen
    Rælingen is a municipality in Viken county, Norway, known for its proximity to Oslo and its mix of residential areas, forests, and lakes.
  • E. Pilestredet
    Pilestredet is a central street in Oslo, Norway, known for hosting parts of Oslo Metropolitan University's main campus and various urban institutions.
  • 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_69e11e4102b881909cf47d3768e25c19 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f13210eb9c8190bc40d06c393e0d9a completed April 28, 2026, 10:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0aae7d32608190bb3d07e7bacc46ac completed May 18, 2026, 6:15 a.m.
NEDg Description generation batch_6a0aaf758ab08190b31a3e6d9db60f68 completed May 18, 2026, 6:19 a.m.
NED2 Entity disambiguation (via description) batch_6a0ab0474af48190b349b3c0595e921a completed May 18, 2026, 6:23 a.m.
Created at: April 16, 2026, 8:38 p.m.