Triple

T7812590
Position Surface form Disambiguated ID Type / Status
Subject Vesoul E180722 entity
Predicate hasDemonym P191 FINISHED
Object Vésulienne
Vésulienne is the French term for a female inhabitant or native of the town of Vesoul in eastern France.
 E694675 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: Vésulienne | Statement: [Vesoul, hasDemonym, Vésulienne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vésulienne
Context triple: [Vesoul, hasDemonym, Vésulienne]
  • A. Maenza
    Maenza is a small historic town in the Lazio region of central Italy, known for its medieval architecture and hilltop setting.
  • B. Vespasia
    Vespasia was an ancient Roman family name associated with the noble lineage of the Vespasii, relatives of the emperor Vespasian.
  • C. Duillier
    Duillier is a small Swiss municipality in the canton of Vaud, located near the town of Nyon in western Switzerland.
  • D. Hoschedé
    Hoschedé is a French surname notably associated with the family closely linked to Impressionist painter Claude Monet.
  • E. Egeria
    Egeria was a late 4th-century Christian pilgrim and travel writer whose detailed account of her journey to the Holy Land provides one of the earliest and most important sources on early Christian worship and pilgrimage practices.
  • 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: Vésulienne
Triple: [Vesoul, hasDemonym, Vésulienne]
Generated description
Vésulienne is the French term for a female inhabitant or native of the town of Vesoul in eastern France.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vésulienne
Target entity description: Vésulienne is the French term for a female inhabitant or native of the town of Vesoul in eastern France.
  • A. Maenza
    Maenza is a small historic town in the Lazio region of central Italy, known for its medieval architecture and hilltop setting.
  • B. Vespasia
    Vespasia was an ancient Roman family name associated with the noble lineage of the Vespasii, relatives of the emperor Vespasian.
  • C. Duillier
    Duillier is a small Swiss municipality in the canton of Vaud, located near the town of Nyon in western Switzerland.
  • D. Hoschedé
    Hoschedé is a French surname notably associated with the family closely linked to Impressionist painter Claude Monet.
  • E. Egeria
    Egeria was a late 4th-century Christian pilgrim and travel writer whose detailed account of her journey to the Holy Land provides one of the earliest and most important sources on early Christian worship and pilgrimage practices.
  • 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_69ca827f6f148190beca4e245b993506 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69caf78e198c81909d4fd227f6b71082 completed March 30, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69cb1472ee908190b073819f3dfad8ee completed March 31, 2026, 12:25 a.m.
NEDg Description generation batch_69cb173190a88190b31fd7973bc19d43 completed March 31, 2026, 12:37 a.m.
NED2 Entity disambiguation (via description) batch_69cb1a56d25881908b8413b82edf5508 completed March 31, 2026, 12:50 a.m.
Created at: March 30, 2026, 4:38 p.m.