Triple
T19677093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | arrondissement of Grenoble |
E472479
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Vizille
Vizille is a historic commune in southeastern France known for its château and its role in the early events of the French Revolution.
|
E1481790
|
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: Vizille | Statement: [arrondissement of Grenoble, contains, Vizille]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vizille Context triple: [arrondissement of Grenoble, contains, Vizille]
-
A.
Guillestre
Guillestre is a small commune in southeastern France’s Hautes-Alpes department, known as a gateway to the Queyras Regional Natural Park and the surrounding Alpine valleys.
-
B.
Vaujours
Vaujours is a small suburban commune in the northeastern outskirts of Paris, France.
-
C.
Brière
Brière is a French-language surname most prominently associated with former NHL player and current hockey executive Daniel Brière.
-
D.
Vouziers
Vouziers is a small commune in northeastern France known for its historical role in World War I and its location in the rural Ardennes region.
-
E.
Ferrière
Ferrière is a French-language surname of Swiss origin borne by various notable individuals, including social worker and humanitarian Suzanne Ferrière.
- 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: Vizille Triple: [arrondissement of Grenoble, contains, Vizille]
Generated description
Vizille is a historic commune in southeastern France known for its château and its role in the early events of the French Revolution.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vizille Target entity description: Vizille is a historic commune in southeastern France known for its château and its role in the early events of the French Revolution.
-
A.
Guillestre
Guillestre is a small commune in southeastern France’s Hautes-Alpes department, known as a gateway to the Queyras Regional Natural Park and the surrounding Alpine valleys.
-
B.
Vaujours
Vaujours is a small suburban commune in the northeastern outskirts of Paris, France.
-
C.
Brière
Brière is a French-language surname most prominently associated with former NHL player and current hockey executive Daniel Brière.
-
D.
Vouziers
Vouziers is a small commune in northeastern France known for its historical role in World War I and its location in the rural Ardennes region.
-
E.
Ferrière
Ferrière is a French-language surname of Swiss origin borne by various notable individuals, including social worker and humanitarian Suzanne Ferrière.
- 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_69d8e514f2e08190ba70a4449519d218 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e641bceef881909c5b655af709c8c6 |
completed | April 20, 2026, 3:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09bb2efdc481908984821f8884e040 |
completed | May 17, 2026, 12:57 p.m. |
| NEDg | Description generation | batch_6a09bea2b9788190b41d3da0b5f3329e |
completed | May 17, 2026, 1:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09bf3466848190b2a5793f5f51efd7 |
completed | May 17, 2026, 1:14 p.m. |
Created at: April 10, 2026, 1:45 p.m.