Triple

T9498663
Position Surface form Disambiguated ID Type / Status
Subject Neumarkt in der Oberpfalz (district) E229077 entity
Predicate contains P35 FINISHED
Object Velburg
Velburg is a small Bavarian town in southeastern Germany known for its historic castle ruins and scenic location in the Upper Palatinate region.
E802096 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: Velburg | Statement: [Neumarkt in der Oberpfalz (district), contains, Velburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Velburg
Context triple: [Neumarkt in der Oberpfalz (district), contains, Velburg]
  • A. Veeningen
    Veeningen is a small village in the Dutch province of Drenthe, located within the municipality of De Wolden.
  • B. Vredenburg
    Vredenburg is a town on South Africa’s West Coast that serves as a regional commercial and service hub near Saldanha Bay.
  • C. Rolandswerth
    Rolandswerth is a district of the German town of Remagen, situated along the Rhine River in the state of Rhineland-Palatinate.
  • D. Woudenberg
    Woudenberg is a small Dutch municipality and town located in the central Netherlands.
  • E. Valkenburg
    Valkenburg is a village in the Dutch province of South Holland, known for its historic charm and proximity to the North Sea coast.
  • 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: Velburg
Triple: [Neumarkt in der Oberpfalz (district), contains, Velburg]
Generated description
Velburg is a small Bavarian town in southeastern Germany known for its historic castle ruins and scenic location in the Upper Palatinate region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Velburg
Target entity description: Velburg is a small Bavarian town in southeastern Germany known for its historic castle ruins and scenic location in the Upper Palatinate region.
  • A. Veeningen
    Veeningen is a small village in the Dutch province of Drenthe, located within the municipality of De Wolden.
  • B. Vredenburg
    Vredenburg is a town on South Africa’s West Coast that serves as a regional commercial and service hub near Saldanha Bay.
  • C. Rolandswerth
    Rolandswerth is a district of the German town of Remagen, situated along the Rhine River in the state of Rhineland-Palatinate.
  • D. Woudenberg
    Woudenberg is a small Dutch municipality and town located in the central Netherlands.
  • E. Valkenburg
    Valkenburg is a village in the Dutch province of South Holland, known for its historic charm and proximity to the North Sea coast.
  • 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_69ca84753660819098e8d416e89e26ae completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd983a94c48190a7ddf95a953c4ecc completed April 1, 2026, 10:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69d12d3aafb88190ac53289039bca88a completed April 4, 2026, 3:24 p.m.
NEDg Description generation batch_69d12dcae2088190bdb4ebac9021e622 completed April 4, 2026, 3:27 p.m.
NED2 Entity disambiguation (via description) batch_69d12e4077a4819094e86eb0de69b2ed completed April 4, 2026, 3:29 p.m.
Created at: March 30, 2026, 7:56 p.m.