Triple

T22962456
Position Surface form Disambiguated ID Type / Status
Subject Lade E570937 entity
Predicate contains P35 FINISHED
Object Ladeparken
Ladeparken is a public park and recreational green space located in the Lade district of Trondheim, Norway.
 E1563096 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: Ladeparken | Statement: [Lade, contains, Ladeparken]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ladeparken
Context triple: [Lade, contains, Ladeparken]
  • A. Parkring
    Parkring is a central boulevard in Vienna that forms part of the historic Ringstrasse, known for its grand architecture and proximity to prominent parks and landmarks.
  • B. Ottopark
    Ottopark is a small urban green space in Berlin’s Moabit district, offering residents a local spot for relaxation and recreation.
  • C. Valbyparken
    Valbyparken is one of Copenhagen’s largest public parks, known for its expansive green spaces, themed gardens, and recreational facilities in the Valby district.
  • D. Parkend
    Parkend is a small village situated within England’s historic Forest of Dean, known for its woodland surroundings and industrial heritage.
  • E. Grøndalsparken
    Grøndalsparken is a public green space in the Copenhagen district of Vanløse, known for its recreational areas and walking paths along the Grøndal stream.
  • 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: Ladeparken
Triple: [Lade, contains, Ladeparken]
Generated description
Ladeparken is a public park and recreational green space located in the Lade district of Trondheim, Norway.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ladeparken
Target entity description: Ladeparken is a public park and recreational green space located in the Lade district of Trondheim, Norway.
  • A. Parkring
    Parkring is a central boulevard in Vienna that forms part of the historic Ringstrasse, known for its grand architecture and proximity to prominent parks and landmarks.
  • B. Ottopark
    Ottopark is a small urban green space in Berlin’s Moabit district, offering residents a local spot for relaxation and recreation.
  • C. Valbyparken
    Valbyparken is one of Copenhagen’s largest public parks, known for its expansive green spaces, themed gardens, and recreational facilities in the Valby district.
  • D. Parkend
    Parkend is a small village situated within England’s historic Forest of Dean, known for its woodland surroundings and industrial heritage.
  • E. Grøndalsparken
    Grøndalsparken is a public green space in the Copenhagen district of Vanløse, known for its recreational areas and walking paths along the Grøndal stream.
  • 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_69e245b212a88190b5259caf51606084 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f181f594fc8190816418486b798198 completed April 29, 2026, 3:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0bca22caa88190889e965c78477a96 completed May 19, 2026, 2:25 a.m.
NEDg Description generation batch_6a0bcb1904c88190ac32e5709bc95858 completed May 19, 2026, 2:29 a.m.
NED2 Entity disambiguation (via description) batch_6a0bcbb8793881909621b16c9ad51cbb completed May 19, 2026, 2:32 a.m.
Created at: April 17, 2026, 3:47 p.m.