Triple

T4680296
Position Surface form Disambiguated ID Type / Status
Subject Alès E103781 entity
Predicate demonym P191 FINISHED
Object Alésienne
Alésienne is the French term for a female inhabitant or native of the town of Alès in southern France.
 E461857 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: Alésienne | Statement: [Alès, demonym, Alésienne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Alésienne
Context triple: [Alès, demonym, Alésienne]
  • A. Bielle
    Bielle is a small traditional village in southwestern France’s Ossau Valley, known for its pastoral Pyrenean setting and historic rural architecture.
  • B. Peillon
    Peillon is a picturesque medieval hilltop village in southeastern France, known for its narrow streets, stone houses, and views over the surrounding Alpes-Maritimes countryside.
  • C. Bongrand
    Bongrand is a fictional character in Émile Zola’s novel *L’Œuvre*, depicted as an older, established painter who contrasts with the avant-garde ambitions of the protagonist.
  • D. Labouret
    Labouret is a French surname associated with individuals such as Marie-Louise Élisabeth Labouret.
  • E. Auberjonois
    Auberjonois is a surname most prominently associated with René Auberjonois, an American actor known for roles in film, television, and voice work.
  • 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: Alésienne
Triple: [Alès, demonym, Alésienne]
Generated description
Alésienne is the French term for a female inhabitant or native of the town of Alès in southern France.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Alésienne
Target entity description: Alésienne is the French term for a female inhabitant or native of the town of Alès in southern France.
  • A. Bielle
    Bielle is a small traditional village in southwestern France’s Ossau Valley, known for its pastoral Pyrenean setting and historic rural architecture.
  • B. Peillon
    Peillon is a picturesque medieval hilltop village in southeastern France, known for its narrow streets, stone houses, and views over the surrounding Alpes-Maritimes countryside.
  • C. Bongrand
    Bongrand is a fictional character in Émile Zola’s novel *L’Œuvre*, depicted as an older, established painter who contrasts with the avant-garde ambitions of the protagonist.
  • D. Labouret
    Labouret is a French surname associated with individuals such as Marie-Louise Élisabeth Labouret.
  • E. Auberjonois
    Auberjonois is a surname most prominently associated with René Auberjonois, an American actor known for roles in film, television, and voice work.
  • 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_69bd43debbf08190b4bc372e286ec234 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd636c105081908655ab384f539f38 completed March 20, 2026, 3:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69be03a8b7a88190bfc4f68438694995 completed March 21, 2026, 2:34 a.m.
NEDg Description generation batch_69be0440e7c881908743b7af9b2fa347 completed March 21, 2026, 2:36 a.m.
NED2 Entity disambiguation (via description) batch_69be04e0f1b08190b3e617150e34648c completed March 21, 2026, 2:39 a.m.
Created at: March 20, 2026, 1:16 p.m.