Triple
T11057383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Vernaz |
E261410
|
entity |
| Predicate | mayor |
P185
|
FINISHED |
| Object |
Yves Lavenier
Yves Lavenier is a French local politician who serves as the mayor of the commune of La Vernaz in southeastern France.
|
E1127354
|
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: Yves Lavenier | Statement: [La Vernaz, mayor, Yves Lavenier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yves Lavenier Context triple: [La Vernaz, mayor, Yves Lavenier]
-
A.
André Dewavrin
André Dewavrin was a French military officer and intelligence leader best known for organizing and directing Free French secret services and resistance operations during World War II.
-
B.
Roland Gallois
Roland Gallois is a film editor known for his work on the feature film "Slow West."
-
C.
Jean-Claude Marmier
Jean-Claude Marmier is a French trail-running organizer best known as one of the founders of the prestigious Ultra-Trail du Mont-Blanc mountain ultramarathon.
-
D.
Michel Andrault
Michel Andrault was a prominent French architect known for his influential large-scale housing and urban development projects in the late 20th century.
-
E.
Pierre Legoupil
Pierre Legoupil was a French naval officer or explorer after whom Cape Legoupil in Antarctica was named, reflecting his role in early polar exploration.
- 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: Yves Lavenier Triple: [La Vernaz, mayor, Yves Lavenier]
Generated description
Yves Lavenier is a French local politician who serves as the mayor of the commune of La Vernaz in southeastern France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yves Lavenier Target entity description: Yves Lavenier is a French local politician who serves as the mayor of the commune of La Vernaz in southeastern France.
-
A.
André Dewavrin
André Dewavrin was a French military officer and intelligence leader best known for organizing and directing Free French secret services and resistance operations during World War II.
-
B.
Roland Gallois
Roland Gallois is a film editor known for his work on the feature film "Slow West."
-
C.
Jean-Claude Marmier
Jean-Claude Marmier is a French trail-running organizer best known as one of the founders of the prestigious Ultra-Trail du Mont-Blanc mountain ultramarathon.
-
D.
Michel Andrault
Michel Andrault was a prominent French architect known for his influential large-scale housing and urban development projects in the late 20th century.
-
E.
Pierre Legoupil
Pierre Legoupil was a French naval officer or explorer after whom Cape Legoupil in Antarctica was named, reflecting his role in early polar exploration.
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d798a2404c819090cb0825a67a64fa |
completed | April 9, 2026, 12:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe729766008190803c1dfef2c8c872 |
completed | May 8, 2026, 11:32 p.m. |
| NEDg | Description generation | batch_69fe73ee0da48190b8909009e0dc517b |
completed | May 8, 2026, 11:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe748da7948190b7253b9dc09ae9fa |
completed | May 8, 2026, 11:41 p.m. |
Created at: April 8, 2026, 9:26 p.m.