Triple

T6793639
Position Surface form Disambiguated ID Type / Status
Subject South Limburg E155996 entity
Predicate contains P35 FINISHED
Object Meerssen
Meerssen is a historic town and municipality in the Dutch province of Limburg, known for its medieval basilica and scenic location near Maastricht.
 E691114 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: Meerssen | Statement: [South Limburg, contains, Meerssen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Meerssen
Context triple: [South Limburg, contains, Meerssen]
  • A. Maasbracht
    Maasbracht is a town in the Dutch province of Limburg, known as an inland port and industrial center along the River Meuse.
  • B. Wateringen
    Wateringen is a town in the western Netherlands that forms part of the municipality of Westland in the province of South Holland.
  • C. Zierikzee
    Zierikzee is a historic Dutch town on the island of Schouwen-Duiveland in Zeeland, known for its well-preserved medieval center and maritime heritage.
  • D. Sommelsdijk
    Sommelsdijk is a village in the Netherlands located on the island of Goeree-Overflakkee in the province of South Holland.
  • E. Maarssen
    Maarssen is a town in the Dutch province of Utrecht, situated along the river Vecht and functioning largely as a residential and commuter community near the city of Utrecht.
  • 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: Meerssen
Triple: [South Limburg, contains, Meerssen]
Generated description
Meerssen is a historic town and municipality in the Dutch province of Limburg, known for its medieval basilica and scenic location near Maastricht.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Meerssen
Target entity description: Meerssen is a historic town and municipality in the Dutch province of Limburg, known for its medieval basilica and scenic location near Maastricht.
  • A. Maasbracht
    Maasbracht is a town in the Dutch province of Limburg, known as an inland port and industrial center along the River Meuse.
  • B. Wateringen
    Wateringen is a town in the western Netherlands that forms part of the municipality of Westland in the province of South Holland.
  • C. Zierikzee
    Zierikzee is a historic Dutch town on the island of Schouwen-Duiveland in Zeeland, known for its well-preserved medieval center and maritime heritage.
  • D. Sommelsdijk
    Sommelsdijk is a village in the Netherlands located on the island of Goeree-Overflakkee in the province of South Holland.
  • E. Maarssen
    Maarssen is a town in the Dutch province of Utrecht, situated along the river Vecht and functioning largely as a residential and commuter community near the city of Utrecht.
  • 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_69c6881844448190a65822d9b39d7f88 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d2af6f908190809e39b73894e513 completed March 27, 2026, 6:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69c968c9a1a48190b6ea5bb08ff745af completed March 29, 2026, 6 p.m.
NEDg Description generation batch_69c969a21a38819080fdec02bba37248 completed March 29, 2026, 6:04 p.m.
NED2 Entity disambiguation (via description) batch_69c96ae5f18c8190ab64ad0f10a0036f completed March 29, 2026, 6:09 p.m.
Created at: March 27, 2026, 2:15 p.m.