Triple

T1474176
Position Surface form Disambiguated ID Type / Status
Subject Calais E30799 entity
Predicate hasDemonym P191 FINISHED
Object Calaisienne
Calaisienne is the French term for a female inhabitant or native of the port city of Calais in northern France.
 E170115 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: Calaisienne | Statement: [Calais, hasDemonym, Calaisienne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Calaisienne
Context triple: [Calais, hasDemonym, Calaisienne]
  • A. La Muette
    La Muette is an affluent residential neighborhood in Paris’s 16th arrondissement, known for its embassies, elegant Haussmannian buildings, and proximity to the Bois de Boulogne.
  • 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. Sauvy
    Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
  • D. Répons
    Répons is a groundbreaking 1981–84 composition by Pierre Boulez for chamber ensemble, soloists, and live electronics, renowned for its spatialized sound and innovative use of real-time electronic transformation.
  • E. Peney-Dessous
    Peney-Dessous is a small village in the municipality of Satigny in the canton of Geneva, Switzerland.
  • 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: Calaisienne
Triple: [Calais, hasDemonym, Calaisienne]
Generated description
Calaisienne is the French term for a female inhabitant or native of the port city of Calais in northern France.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Calaisienne
Target entity description: Calaisienne is the French term for a female inhabitant or native of the port city of Calais in northern France.
  • A. La Muette
    La Muette is an affluent residential neighborhood in Paris’s 16th arrondissement, known for its embassies, elegant Haussmannian buildings, and proximity to the Bois de Boulogne.
  • 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. Sauvy
    Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
  • D. Répons
    Répons is a groundbreaking 1981–84 composition by Pierre Boulez for chamber ensemble, soloists, and live electronics, renowned for its spatialized sound and innovative use of real-time electronic transformation.
  • E. Peney-Dessous
    Peney-Dessous is a small village in the municipality of Satigny in the canton of Geneva, Switzerland.
  • 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_69a498fe55a88190ab7f9e40ace88e49 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c6011d248190988380eca4ecf514 completed March 1, 2026, 11:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad1c9fb9e48190b904440cb7229c8f completed March 8, 2026, 6:52 a.m.
NEDg Description generation batch_69ad1d7451248190b110814a30270b22 completed March 8, 2026, 6:55 a.m.
NED2 Entity disambiguation (via description) batch_69ad1dccba7081908f24eef1b9ad5f09 completed March 8, 2026, 6:57 a.m.
Created at: March 1, 2026, 8:11 p.m.