Triple

T5484037
Position Surface form Disambiguated ID Type / Status
Subject Maine-et-Loire E123533 entity
Predicate contains P35 FINISHED
Object Doué-en-Anjou
Doué-en-Anjou is a commune in western France known for its troglodyte dwellings and wine production in the Maine-et-Loire department of the Pays de la Loire region.
 E526465 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: Doué-en-Anjou | Statement: [Maine-et-Loire, contains, Doué-en-Anjou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Doué-en-Anjou
Context triple: [Maine-et-Loire, contains, Doué-en-Anjou]
  • A. Châteaubriant
    Châteaubriant is a historic town in western France known for its medieval castle and role as a local administrative and cultural center.
  • B. Alençon
    Alençon is a historic town in northwestern France renowned for its fine lace-making tradition and architectural heritage.
  • C. Sully-sur-Loire
    Sully-sur-Loire is a historic commune in north-central France known for its medieval château overlooking the Loire River.
  • D. Saumur
    Saumur is a historic town in western France renowned for its château, wine production, and cavalry school on the banks of the Loire River.
  • E. Chapeauroux
    Chapeauroux is a river in central France that flows through the Massif Central before joining the Allier.
  • 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: Doué-en-Anjou
Triple: [Maine-et-Loire, contains, Doué-en-Anjou]
Generated description
Doué-en-Anjou is a commune in western France known for its troglodyte dwellings and wine production in the Maine-et-Loire department of the Pays de la Loire region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Doué-en-Anjou
Target entity description: Doué-en-Anjou is a commune in western France known for its troglodyte dwellings and wine production in the Maine-et-Loire department of the Pays de la Loire region.
  • A. Châteaubriant
    Châteaubriant is a historic town in western France known for its medieval castle and role as a local administrative and cultural center.
  • B. Alençon
    Alençon is a historic town in northwestern France renowned for its fine lace-making tradition and architectural heritage.
  • C. Sully-sur-Loire
    Sully-sur-Loire is a historic commune in north-central France known for its medieval château overlooking the Loire River.
  • D. Saumur
    Saumur is a historic town in western France renowned for its château, wine production, and cavalry school on the banks of the Loire River.
  • E. Chapeauroux
    Chapeauroux is a river in central France that flows through the Massif Central before joining the Allier.
  • 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_69bd4648883481909e9775d43300c5fa completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd925deadc81908e193eeb75b63d90 completed March 20, 2026, 6:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69bfbd5d67c881909b57ead8968a840b completed March 22, 2026, 9:58 a.m.
NEDg Description generation batch_69bfbe2a1a2c8190a4bc29759251ee37 completed March 22, 2026, 10:02 a.m.
NED2 Entity disambiguation (via description) batch_69bfbe9cf5d481908ca6f1b49e54d9bd completed March 22, 2026, 10:04 a.m.
Created at: March 20, 2026, 2:10 p.m.