Triple
T9683447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charolles |
E234344
|
entity |
| Predicate | hasDemonym |
P191
|
FINISHED |
| Object |
Charollais
Charollais is the French demonym referring to inhabitants of the town of Charolles in the Saône-et-Loire department of eastern France.
|
E814746
|
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: Charollais | Statement: [Charolles, hasDemonym, Charollais]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charollais Context triple: [Charolles, hasDemonym, Charollais]
-
A.
Auvergnat
Auvergnat is a variety of the Occitan language traditionally spoken in France’s Auvergne region and surrounding areas.
-
B.
Auberjonois
Auberjonois is a surname most prominently associated with René Auberjonois, an American actor known for roles in film, television, and voice work.
-
C.
Ambertois
Ambertois is the French demonym for inhabitants of the town of Ambert in central France.
-
D.
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.
-
E.
Cigales
Cigales is a small town in the province of Valladolid, Spain, known historically as a royal residence and for its wine production.
- 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: Charollais Triple: [Charolles, hasDemonym, Charollais]
Generated description
Charollais is the French demonym referring to inhabitants of the town of Charolles in the Saône-et-Loire department of eastern France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Charollais Target entity description: Charollais is the French demonym referring to inhabitants of the town of Charolles in the Saône-et-Loire department of eastern France.
-
A.
Auvergnat
Auvergnat is a variety of the Occitan language traditionally spoken in France’s Auvergne region and surrounding areas.
-
B.
Auberjonois
Auberjonois is a surname most prominently associated with René Auberjonois, an American actor known for roles in film, television, and voice work.
-
C.
Ambertois
Ambertois is the French demonym for inhabitants of the town of Ambert in central France.
-
D.
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.
-
E.
Cigales
Cigales is a small town in the province of Valladolid, Spain, known historically as a royal residence and for its wine production.
- 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_69ca84c99e34819092e5563a7106cfca |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9ccf21a08190a1302b933b9e50be |
completed | April 1, 2026, 10:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1910192e88190b10409ae62c1c948 |
completed | April 4, 2026, 10:30 p.m. |
| NEDg | Description generation | batch_69d19327f0b481908be85bcb0deccb46 |
completed | April 4, 2026, 10:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d193fac390819092dd913dc78e2841 |
completed | April 4, 2026, 10:43 p.m. |
Created at: March 30, 2026, 8:16 p.m.