Triple

T7224104
Position Surface form Disambiguated ID Type / Status
Subject Centre Department E150333 entity
Predicate hasSettlement P1068 FINISHED
Object Belladère
Belladère is a commune and border town in central Haiti, located near the frontier with the Dominican Republic.
 E650160 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: Belladère | Statement: [Centre Department, hasSettlement, Belladère]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Belladère
Context triple: [Centre Department, hasSettlement, Belladère]
  • A. Dora di Veny
    Dora di Veny is a mountain stream in Italy’s Aosta Valley that drains the southern side of Mont Blanc and contributes to the upper course of the Dora Baltea river.
  • B. Margeride
    Margeride is a mountainous and sparsely populated region in south-central France known for its granite plateaus, forests, and traditional rural landscapes.
  • C. Méjanelle
    Méjanelle is a French wine-producing area recognized as a subregion within the broader Languedoc appellation in southern France.
  • D. La Rosalie
    La Rosalie is a 19th-century French melody that later served as the musical basis for Malaysia’s national anthem, "Negaraku."
  • E. Rosabella
    Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
  • 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: Belladère
Triple: [Centre Department, hasSettlement, Belladère]
Generated description
Belladère is a commune and border town in central Haiti, located near the frontier with the Dominican Republic.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Belladère
Target entity description: Belladère is a commune and border town in central Haiti, located near the frontier with the Dominican Republic.
  • A. Dora di Veny
    Dora di Veny is a mountain stream in Italy’s Aosta Valley that drains the southern side of Mont Blanc and contributes to the upper course of the Dora Baltea river.
  • B. Margeride
    Margeride is a mountainous and sparsely populated region in south-central France known for its granite plateaus, forests, and traditional rural landscapes.
  • C. Méjanelle
    Méjanelle is a French wine-producing area recognized as a subregion within the broader Languedoc appellation in southern France.
  • D. La Rosalie
    La Rosalie is a 19th-century French melody that later served as the musical basis for Malaysia’s national anthem, "Negaraku."
  • E. Rosabella
    Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
  • 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_69c687effb44819092b95d07d0368c9f completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6e9db51888190b8463d0003f334fa completed March 27, 2026, 8:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7cc11b48881909886a71f1b887789 completed March 28, 2026, 12:39 p.m.
NEDg Description generation batch_69c7cd47d4b88190a75c419c41d57cb9 completed March 28, 2026, 12:44 p.m.
NED2 Entity disambiguation (via description) batch_69c7cdbdee8081908faadb9cdaa4df13 completed March 28, 2026, 12:46 p.m.
Created at: March 27, 2026, 2:54 p.m.