Triple

T14109736
Position Surface form Disambiguated ID Type / Status
Subject Svanemøllen E339601 entity
Predicate hasStationCode P1289 FINISHED
Object Svm
Svm is the station code used to identify Svanemøllen railway station in Copenhagen’s public transport system.
 E1079515 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: Svm | Statement: [Svanemøllen, hasStationCode, Svm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Svm
Context triple: [Svanemøllen, hasStationCode, Svm]
  • A. libsvm
    libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
  • B. Support Vector Machines
    Support Vector Machines are a class of supervised learning algorithms used primarily for classification and regression tasks, which work by finding the optimal separating hyperplane between data classes in a high-dimensional feature space.
  • C. SVR
    SVR is the set of post-nominal letters used to denote recipients of the Order of the White Rose of Finland.
  • D. SVR
    SVR is the ICAO airline designator assigned to Ural Airlines, a Russian commercial air carrier.
  • E. SVR
    SVR is Russia’s primary foreign intelligence service, which succeeded the Soviet-era KGB’s external intelligence functions after the USSR’s dissolution.
  • 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: Svm
Triple: [Svanemøllen, hasStationCode, Svm]
Generated description
Svm is the station code used to identify Svanemøllen railway station in Copenhagen’s public transport system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Svm
Target entity description: Svm is the station code used to identify Svanemøllen railway station in Copenhagen’s public transport system.
  • A. libsvm
    libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
  • B. Support Vector Machines
    Support Vector Machines are a class of supervised learning algorithms used primarily for classification and regression tasks, which work by finding the optimal separating hyperplane between data classes in a high-dimensional feature space.
  • C. SVR
    SVR is the set of post-nominal letters used to denote recipients of the Order of the White Rose of Finland.
  • D. SVR
    SVR is the ICAO airline designator assigned to Ural Airlines, a Russian commercial air carrier.
  • E. SVR
    SVR is Russia’s primary foreign intelligence service, which succeeded the Soviet-era KGB’s external intelligence functions after the USSR’s dissolution.
  • 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_69d81c69b5c8819094aa1abf18302908 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de600caf308190ab6d8451ed4e3797 completed April 14, 2026, 3:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcd0b699108190993f1102418ecff1 completed May 7, 2026, 5:49 p.m.
NEDg Description generation batch_69fcd2d99c4c8190baf15d470ead7b1c completed May 7, 2026, 5:58 p.m.
NED2 Entity disambiguation (via description) batch_69fcd3853e848190a210d1c8c08bd6cc completed May 7, 2026, 6:01 p.m.
Created at: April 9, 2026, 10:22 p.m.