Triple

T9709289
Position Surface form Disambiguated ID Type / Status
Subject Duchy of Lower Lotharingia E234979 entity
Predicate hasPart P35 FINISHED
Object Maasland
Maasland is a historical region in the Low Countries centered along the river Meuse, known for its medieval political and cultural significance.
 E816527 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: Maasland | Statement: [Duchy of Lower Lotharingia, hasPart, Maasland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Maasland
Context triple: [Duchy of Lower Lotharingia, hasPart, Maasland]
  • A. Zoutelande
    Zoutelande is a coastal village and popular seaside resort in the Dutch province of Zeeland, known for its beaches and dunes along the North Sea.
  • B. Landsmeer
    Landsmeer is a small Dutch town and municipality in North Holland, situated just north of Amsterdam and known for its watery landscapes and nature reserves.
  • C. Kennemerland
    Kennemerland is a coastal historical region in the northwest of the Netherlands, known for its dunes, beaches, and old trading towns.
  • D. Foosland
    Foosland is a small village located in Champaign County, Illinois, United States.
  • E. Westhavelland
    Westhavelland is a rural region in western Brandenburg, Germany, characterized by extensive wetlands, lakes, and protected natural landscapes.
  • 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: Maasland
Triple: [Duchy of Lower Lotharingia, hasPart, Maasland]
Generated description
Maasland is a historical region in the Low Countries centered along the river Meuse, known for its medieval political and cultural significance.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Maasland
Target entity description: Maasland is a historical region in the Low Countries centered along the river Meuse, known for its medieval political and cultural significance.
  • A. Zoutelande
    Zoutelande is a coastal village and popular seaside resort in the Dutch province of Zeeland, known for its beaches and dunes along the North Sea.
  • B. Landsmeer
    Landsmeer is a small Dutch town and municipality in North Holland, situated just north of Amsterdam and known for its watery landscapes and nature reserves.
  • C. Kennemerland
    Kennemerland is a coastal historical region in the northwest of the Netherlands, known for its dunes, beaches, and old trading towns.
  • D. Foosland
    Foosland is a small village located in Champaign County, Illinois, United States.
  • E. Westhavelland
    Westhavelland is a rural region in western Brandenburg, Germany, characterized by extensive wetlands, lakes, and protected natural landscapes.
  • 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_69ca84cd8fa0819090a5e243ceb37003 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9da7c6188190b086f7e411378268 completed April 1, 2026, 10:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69d19f8476e08190865700679069dee6 completed April 4, 2026, 11:32 p.m.
NEDg Description generation batch_69d1a0e096648190b4babe3feb77dae7 completed April 4, 2026, 11:38 p.m.
NED2 Entity disambiguation (via description) batch_69d1a184af3081908ce2932218244e7d completed April 4, 2026, 11:40 p.m.
Created at: March 30, 2026, 8:19 p.m.