Triple

T9714405
Position Surface form Disambiguated ID Type / Status
Subject Landes forest E235103 entity
Predicate partOf P40 FINISHED
Object Les Landes
Les Landes is a region in southwestern France known for its vast Atlantic coastline, extensive pine forests, and rural landscapes.
 E817035 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: Les Landes | Statement: [Landes forest, partOf, Les Landes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Les Landes
Context triple: [Landes forest, partOf, Les Landes]
  • A. Sologne
    Sologne is a rural region in central France known for its forests, lakes, and hunting estates.
  • B. Camargue
    Camargue is a vast wetland region in southern France known for its salt marshes, wild white horses, black bulls, and rich birdlife including flamingos.
  • C. Vexin Français Regional Natural Park
    Vexin Français Regional Natural Park is a protected rural landscape in northern France known for its traditional villages, farmland, and natural heritage near the Paris region.
  • D. Forêt de Retz
    Forêt de Retz is a large historic forest in northern France known for its extensive woodlands, biodiversity, and long-standing role as a royal hunting ground.
  • E. Dombes
    Dombes is a historic rural region in eastern France known for its many ponds, wetlands, and traditional fish farming.
  • 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: Les Landes
Triple: [Landes forest, partOf, Les Landes]
Generated description
Les Landes is a region in southwestern France known for its vast Atlantic coastline, extensive pine forests, and rural landscapes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Les Landes
Target entity description: Les Landes is a region in southwestern France known for its vast Atlantic coastline, extensive pine forests, and rural landscapes.
  • A. Sologne
    Sologne is a rural region in central France known for its forests, lakes, and hunting estates.
  • B. Camargue
    Camargue is a vast wetland region in southern France known for its salt marshes, wild white horses, black bulls, and rich birdlife including flamingos.
  • C. Vexin Français Regional Natural Park
    Vexin Français Regional Natural Park is a protected rural landscape in northern France known for its traditional villages, farmland, and natural heritage near the Paris region.
  • D. Forêt de Retz
    Forêt de Retz is a large historic forest in northern France known for its extensive woodlands, biodiversity, and long-standing role as a royal hunting ground.
  • E. Dombes
    Dombes is a historic rural region in eastern France known for its many ponds, wetlands, and traditional fish farming.
  • 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_69cd9e0a1b548190b34c8571751ca1d3 completed April 1, 2026, 10:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69d19f900ca08190a52f042feab2afa3 completed April 4, 2026, 11:32 p.m.
NEDg Description generation batch_69d1a181f10081908bfb0fae5a08462f completed April 4, 2026, 11:40 p.m.
NED2 Entity disambiguation (via description) batch_69d1a228b8488190a5a61f3468e92649 completed April 4, 2026, 11:43 p.m.
Created at: March 30, 2026, 8:19 p.m.