Triple

T346894
Position Surface form Disambiguated ID Type / Status
Subject Île-de-France E6961 entity
Predicate contains P35 FINISHED
Object Val-d'Oise
Val-d'Oise is a department in northern France that forms part of the Paris metropolitan region and includes both suburban areas and rural landscapes.
 E45085 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: Val-d'Oise | Statement: [Île-de-France, contains, Val-d'Oise]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Val-d'Oise
Context triple: [Île-de-France, contains, Val-d'Oise]
  • A. Aube
    Aube is a department in northeastern France known for its historic towns, Champagne vineyards, and rural landscapes.
  • B. Creuse
    Creuse is a rural department in central France known for its sparsely populated landscapes, traditional agriculture, and part of the historic Limousin region.
  • C. Yonne
    Yonne is a major river in north-central France that flows through the Burgundy region before joining the Seine.
  • D. Nièvre
    Nièvre is a rural department in central France’s Bourgogne-Franche-Comté region, known for its rolling countryside, the Loire River, and its capital city Nevers.
  • E. Yvelines
    Yvelines is a department in north-central France, west of Paris, known for the Palace of Versailles and its mix of historic towns, forests, and affluent suburbs.
  • 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: Val-d'Oise
Triple: [Île-de-France, contains, Val-d'Oise]
Generated description
Val-d'Oise is a department in northern France that forms part of the Paris metropolitan region and includes both suburban areas and rural landscapes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Val-d'Oise
Target entity description: Val-d'Oise is a department in northern France that forms part of the Paris metropolitan region and includes both suburban areas and rural landscapes.
  • A. Aube
    Aube is a department in northeastern France known for its historic towns, Champagne vineyards, and rural landscapes.
  • B. Creuse
    Creuse is a rural department in central France known for its sparsely populated landscapes, traditional agriculture, and part of the historic Limousin region.
  • C. Yonne
    Yonne is a major river in north-central France that flows through the Burgundy region before joining the Seine.
  • D. Nièvre
    Nièvre is a rural department in central France’s Bourgogne-Franche-Comté region, known for its rolling countryside, the Loire River, and its capital city Nevers.
  • E. Yvelines
    Yvelines is a department in north-central France, west of Paris, known for the Palace of Versailles and its mix of historic towns, forests, and affluent suburbs.
  • 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_69a2e7951ba08190960e90823b5078f3 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2eb1a37c08190b1380f6bf8513a37 completed Feb. 28, 2026, 1:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3e014e2748190b4c45b16186a0d66 completed March 1, 2026, 6:43 a.m.
NEDg Description generation batch_69a3e08a1b00819089a278b25d5c2a8f completed March 1, 2026, 6:45 a.m.
NED2 Entity disambiguation (via description) batch_69a3e1a5e2ec8190a5e7145ff36ace2e completed March 1, 2026, 6:50 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.