Triple

T9432222
Position Surface form Disambiguated ID Type / Status
Subject Ambert E227407 entity
Predicate hasDemonym P191 FINISHED
Object Ambertois
Ambertois is the French demonym for inhabitants of the town of Ambert in central France.
 E799862 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: Ambertois | Statement: [Ambert, hasDemonym, Ambertois]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ambertois
Context triple: [Ambert, hasDemonym, Ambertois]
  • A. Amiénois
    Amiénois is a regional variety of the Picard language traditionally spoken in and around the city of Amiens in northern France.
  • B. Soissonnais
    Soissonnais is a historical region in northern France centered around the city of Soissons, known for its early medieval significance and role in the Frankish kingdom.
  • C. Montévrain
    Montévrain is a suburban commune in the eastern outskirts of Paris, France, known for its proximity to Disneyland Paris and its role in the Marne-la-Vallée new town development.
  • D. Vendômois
    Vendômois is the French demonym referring to inhabitants or natives of the town of Vendôme in central France.
  • 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: Ambertois
Triple: [Ambert, hasDemonym, Ambertois]
Generated description
Ambertois is the French demonym for inhabitants of the town of Ambert in central France.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ambertois
Target entity description: Ambertois is the French demonym for inhabitants of the town of Ambert in central France.
  • A. Amiénois
    Amiénois is a regional variety of the Picard language traditionally spoken in and around the city of Amiens in northern France.
  • B. Soissonnais
    Soissonnais is a historical region in northern France centered around the city of Soissons, known for its early medieval significance and role in the Frankish kingdom.
  • C. Montévrain
    Montévrain is a suburban commune in the eastern outskirts of Paris, France, known for its proximity to Disneyland Paris and its role in the Marne-la-Vallée new town development.
  • D. Vendômois
    Vendômois is the French demonym referring to inhabitants or natives of the town of Vendôme in central France.
  • 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_69ca8437a7ac81908651de48f2d2141d completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd7e6059bc8190a7e98aef3caabd0b completed April 1, 2026, 8:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1104033c08190a3670b017bd984d5 completed April 4, 2026, 1:21 p.m.
NEDg Description generation batch_69d110ffda7881908e4edd692b818464 completed April 4, 2026, 1:24 p.m.
NED2 Entity disambiguation (via description) batch_69d11190dd6c8190b6318df44daa9858 completed April 4, 2026, 1:26 p.m.
Created at: March 30, 2026, 7:49 p.m.