Triple

T1848843
Position Surface form Disambiguated ID Type / Status
Subject Southwestern France E41345 entity
Predicate containsWineRegion P8842 FINISHED
Object Bergerac
Bergerac is a notable wine-producing area in southwestern France, recognized for its diverse red, white, and dessert wines.
 E206236 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: Bergerac | Statement: [Southwestern France, containsWineRegion, Bergerac]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bergerac
Context triple: [Southwestern France, containsWineRegion, Bergerac]
  • A. Château Rouge
    Château Rouge is a Paris Métro station in the 18th arrondissement, serving the multicultural Château Rouge neighborhood near Montmartre.
  • 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. Le Breton
    Le Breton is a French surname borne by various notable figures, including publishers, politicians, and artists.
  • D. Le Lavandou
    Le Lavandou is a seaside resort town on the French Riviera in southeastern France, known for its sandy beaches and Mediterranean coastal scenery.
  • E. Aligoté
    Aligoté is a white grape variety from Burgundy known for producing light, crisp, and high-acid wines often enjoyed young.
  • 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: Bergerac
Triple: [Southwestern France, containsWineRegion, Bergerac]
Generated description
Bergerac is a notable wine-producing area in southwestern France, recognized for its diverse red, white, and dessert wines.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bergerac
Target entity description: Bergerac is a notable wine-producing area in southwestern France, recognized for its diverse red, white, and dessert wines.
  • A. Château Rouge
    Château Rouge is a Paris Métro station in the 18th arrondissement, serving the multicultural Château Rouge neighborhood near Montmartre.
  • 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. Le Breton
    Le Breton is a French surname borne by various notable figures, including publishers, politicians, and artists.
  • D. Le Lavandou
    Le Lavandou is a seaside resort town on the French Riviera in southeastern France, known for its sandy beaches and Mediterranean coastal scenery.
  • E. Aligoté
    Aligoté is a white grape variety from Burgundy known for producing light, crisp, and high-acid wines often enjoyed young.
  • 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_69a88648cd44819093303206d96d76ad completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb06570248190ae2c1f1716f3aa97 completed March 7, 2026, 4:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69adc9c5852081909e178b7e55c11bc7 completed March 8, 2026, 7:11 p.m.
NEDg Description generation batch_69adcaf1917c819090eac27de62494ca completed March 8, 2026, 7:16 p.m.
NED2 Entity disambiguation (via description) batch_69adcbba64588190aa0ebd2b6f67afa7 completed March 8, 2026, 7:19 p.m.
Created at: March 4, 2026, 7:33 p.m.