Triple
T11817251
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vaux-sur-Sûre |
E281032
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Poisson-Moulin
Poisson-Moulin is a small locality within the municipality of Vaux-sur-Sûre in the Walloon region of Belgium.
|
E948116
|
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: Poisson-Moulin | Statement: [Vaux-sur-Sûre, hasSubdivision, Poisson-Moulin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Poisson-Moulin Context triple: [Vaux-sur-Sûre, hasSubdivision, Poisson-Moulin]
-
A.
Mouton-Duvernet
Mouton-Duvernet is a Paris Métro station in the 14th arrondissement, serving the Montparnasse area and named after the French general Régis Barthélemy Mouton-Duvernet.
-
B.
Surpierre
Surpierre is a small municipality in the canton of Fribourg in western Switzerland.
-
C.
Berthelot
Berthelot is a French surname most notably associated with the influential 19th-century chemist and politician Marcelin Berthelot.
-
D.
Langevin
Langevin is a French surname most notably associated with physicist Paul Langevin and several other prominent figures in science and public life.
-
E.
Serre-Chevalier
Serre-Chevalier is a major ski resort in the French Alps, renowned for its extensive slopes, sunny climate, and traditional mountain villages.
- 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: Poisson-Moulin Triple: [Vaux-sur-Sûre, hasSubdivision, Poisson-Moulin]
Generated description
Poisson-Moulin is a small locality within the municipality of Vaux-sur-Sûre in the Walloon region of Belgium.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Poisson-Moulin Target entity description: Poisson-Moulin is a small locality within the municipality of Vaux-sur-Sûre in the Walloon region of Belgium.
-
A.
Mouton-Duvernet
Mouton-Duvernet is a Paris Métro station in the 14th arrondissement, serving the Montparnasse area and named after the French general Régis Barthélemy Mouton-Duvernet.
-
B.
Surpierre
Surpierre is a small municipality in the canton of Fribourg in western Switzerland.
-
C.
Berthelot
Berthelot is a French surname most notably associated with the influential 19th-century chemist and politician Marcelin Berthelot.
-
D.
Langevin
Langevin is a French surname most notably associated with physicist Paul Langevin and several other prominent figures in science and public life.
-
E.
Serre-Chevalier
Serre-Chevalier is a major ski resort in the French Alps, renowned for its extensive slopes, sunny climate, and traditional mountain villages.
- 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_69d6ab26aae88190b2489efcb2a24234 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a5e760988190b50d13bba5ef5b43 |
completed | April 10, 2026, 7:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f131cbf9708190ba8394fb3508b975 |
completed | April 28, 2026, 10:16 p.m. |
| NEDg | Description generation | batch_69f14e8a1b788190a1704d6e102342e3 |
completed | April 29, 2026, 12:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f15715a1588190ba0ec21647adc57c |
completed | April 29, 2026, 12:55 a.m. |
Created at: April 8, 2026, 9:42 p.m.