Triple

T15937410
Position Surface form Disambiguated ID Type / Status
Subject Colonies of Benevolence E386472 entity
Predicate hasPart P35 FINISHED
Object Ommerschans
Ommerschans is a former Dutch fortified settlement that became one of the 19th-century Colonies of Benevolence, where the poor and vagrants were resettled in an experimental agricultural community.
 E1184454 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: Ommerschans | Statement: [Colonies of Benevolence, hasPart, Ommerschans]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ommerschans
Context triple: [Colonies of Benevolence, hasPart, Ommerschans]
  • A. Oesling
    Oesling is the sparsely populated, hilly and forested northern region of Luxembourg, forming part of the Ardennes.
  • B. Maasbracht
    Maasbracht is a town in the Dutch province of Limburg, known as an inland port and industrial center along the River Meuse.
  • C. Renswoude
    Renswoude is a small rural municipality in the Dutch province of Utrecht, known for its historic estates and agricultural landscape.
  • D. Ougrée
    Ougrée is a town in the Walloon region of Belgium that forms a sub-municipality of the industrial city of Seraing in the province of Liège.
  • E. Woudenberg
    Woudenberg is a small Dutch municipality and town located in the central Netherlands.
  • 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: Ommerschans
Triple: [Colonies of Benevolence, hasPart, Ommerschans]
Generated description
Ommerschans is a former Dutch fortified settlement that became one of the 19th-century Colonies of Benevolence, where the poor and vagrants were resettled in an experimental agricultural community.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ommerschans
Target entity description: Ommerschans is a former Dutch fortified settlement that became one of the 19th-century Colonies of Benevolence, where the poor and vagrants were resettled in an experimental agricultural community.
  • A. Oesling
    Oesling is the sparsely populated, hilly and forested northern region of Luxembourg, forming part of the Ardennes.
  • B. Maasbracht
    Maasbracht is a town in the Dutch province of Limburg, known as an inland port and industrial center along the River Meuse.
  • C. Renswoude
    Renswoude is a small rural municipality in the Dutch province of Utrecht, known for its historic estates and agricultural landscape.
  • D. Ougrée
    Ougrée is a town in the Walloon region of Belgium that forms a sub-municipality of the industrial city of Seraing in the province of Liège.
  • E. Woudenberg
    Woudenberg is a small Dutch municipality and town located in the central Netherlands.
  • 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_69d86da750008190987eb26be3f6c118 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e156ab7f548190b2d1aafa0e6d2c24 completed April 16, 2026, 9:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffb5b8121881909b15bf6451d3d3a8 completed May 9, 2026, 10:31 p.m.
NEDg Description generation batch_69ffb718d60481908ac0034ed8d8abc5 completed May 9, 2026, 10:37 p.m.
NED2 Entity disambiguation (via description) batch_69ffb7c98cf8819097c7012040dbfe89 completed May 9, 2026, 10:40 p.m.
Created at: April 10, 2026, 4:53 a.m.