Triple
T2175468
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Drôme |
E48515
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Saint-Paul-Trois-Châteaux
Saint-Paul-Trois-Châteaux is a historic commune in southeastern France known for its medieval architecture and Romanesque cathedral.
|
E242866
|
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: Saint-Paul-Trois-Châteaux | Statement: [Drôme, contains, Saint-Paul-Trois-Châteaux]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saint-Paul-Trois-Châteaux Context triple: [Drôme, contains, Saint-Paul-Trois-Châteaux]
-
A.
Guéret
Guéret is a small city in central France that serves as the capital of the Creuse department in the Nouvelle-Aquitaine region.
-
B.
Lussac-les-Châteaux
Lussac-les-Châteaux is a small commune in western France, notable as the birthplace of the influential 17th-century royal mistress Madame de Montespan.
-
C.
Châteauroux
Châteauroux is a city in central France that will host the shooting events for the 2024 Summer Olympics.
-
D.
Montluçon
Montluçon is a historic industrial town in central France known for its medieval old quarter and role as a key urban center in the Allier department.
-
E.
Bourgueil
Bourgueil is a Loire Valley wine appellation in France renowned for its red wines, particularly those made predominantly from Cabernet Franc.
- 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: Saint-Paul-Trois-Châteaux Triple: [Drôme, contains, Saint-Paul-Trois-Châteaux]
Generated description
Saint-Paul-Trois-Châteaux is a historic commune in southeastern France known for its medieval architecture and Romanesque cathedral.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Saint-Paul-Trois-Châteaux Target entity description: Saint-Paul-Trois-Châteaux is a historic commune in southeastern France known for its medieval architecture and Romanesque cathedral.
-
A.
Guéret
Guéret is a small city in central France that serves as the capital of the Creuse department in the Nouvelle-Aquitaine region.
-
B.
Lussac-les-Châteaux
Lussac-les-Châteaux is a small commune in western France, notable as the birthplace of the influential 17th-century royal mistress Madame de Montespan.
-
C.
Châteauroux
Châteauroux is a city in central France that will host the shooting events for the 2024 Summer Olympics.
-
D.
Montluçon
Montluçon is a historic industrial town in central France known for its medieval old quarter and role as a key urban center in the Allier department.
-
E.
Bourgueil
Bourgueil is a Loire Valley wine appellation in France renowned for its red wines, particularly those made predominantly from Cabernet Franc.
- 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_69a88aa3faa48190995b233af6525815 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abbece30888190936853740ff6cb02 |
completed | March 7, 2026, 5:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae5d9eff988190a02734bd73616cba |
completed | March 9, 2026, 5:41 a.m. |
| NEDg | Description generation | batch_69ae5e5f023081909cd046b5850f8026 |
completed | March 9, 2026, 5:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae5ef99018819083a778378ea493e8 |
completed | March 9, 2026, 5:47 a.m. |
Created at: March 4, 2026, 7:45 p.m.