Triple

T17686907
Position Surface form Disambiguated ID Type / Status
Subject Gribskov Line E440915 entity
Predicate connects P390 FINISHED
Object Vejby
Vejby is a small town in North Zealand, Denmark, known for its rural surroundings and access to the Gribskov railway line.
 E1282770 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: Vejby | Statement: [Gribskov Line, connects, Vejby]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vejby
Context triple: [Gribskov Line, connects, Vejby]
  • A. Varel
    Varel is a coastal town in northwestern Germany known for its location near the Jade Bight and its mix of maritime industry and tourism.
  • B. Vendryně
    Vendryně is a village in the Moravian-Silesian Region of the Czech Republic, known for its location in the historical region of Cieszyn Silesia near the Olza River.
  • C. Valtice
    Valtice is a historic town in the South Moravian Region of the Czech Republic, renowned for its Baroque chateau, vineyards, and role as a key part of the Lednice–Valtice cultural landscape.
  • D. Sviblovo
    Sviblovo is a Moscow Metro station on the Kaluzhsko–Rizhskaya Line serving the Sviblovo District in the city’s northeast.
  • E. Veitvet
    Veitvet is a residential neighborhood in Oslo, Norway, known for its apartment blocks, local shopping center, and multicultural community.
  • 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: Vejby
Triple: [Gribskov Line, connects, Vejby]
Generated description
Vejby is a small town in North Zealand, Denmark, known for its rural surroundings and access to the Gribskov railway line.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vejby
Target entity description: Vejby is a small town in North Zealand, Denmark, known for its rural surroundings and access to the Gribskov railway line.
  • A. Varel
    Varel is a coastal town in northwestern Germany known for its location near the Jade Bight and its mix of maritime industry and tourism.
  • B. Vendryně
    Vendryně is a village in the Moravian-Silesian Region of the Czech Republic, known for its location in the historical region of Cieszyn Silesia near the Olza River.
  • C. Valtice
    Valtice is a historic town in the South Moravian Region of the Czech Republic, renowned for its Baroque chateau, vineyards, and role as a key part of the Lednice–Valtice cultural landscape.
  • D. Sviblovo
    Sviblovo is a Moscow Metro station on the Kaluzhsko–Rizhskaya Line serving the Sviblovo District in the city’s northeast.
  • E. Veitvet
    Veitvet is a residential neighborhood in Oslo, Norway, known for its apartment blocks, local shopping center, and multicultural community.
  • 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_69d8b9e940b081908b862bb0e6e89b0d completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e470488c4081909b747313ef97b69c completed April 19, 2026, 6:03 a.m.
NED1 Entity disambiguation (via context triple) batch_6a02232e1b4481908e8a89f2c1c244f1 completed May 11, 2026, 6:42 p.m.
NEDg Description generation batch_6a0224e2403881909a6f86bcad4ce7b3 completed May 11, 2026, 6:50 p.m.
NED2 Entity disambiguation (via description) batch_6a02254cbd3c8190afca986f066a60d2 completed May 11, 2026, 6:51 p.m.
Created at: April 10, 2026, 10:03 a.m.