Triple
T1170249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Escola Nacional de Belas Artes |
E24894
|
entity |
| Predicate | hasNotableAlumni |
P51
|
FINISHED |
| Object |
Guignard
Guignard was a prominent Brazilian painter and art educator known for his lyrical landscapes and significant influence on modern Brazilian art.
|
E139241
|
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: Guignard | Statement: [Escola Nacional de Belas Artes, hasNotableAlumni, Guignard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guignard Context triple: [Escola Nacional de Belas Artes, hasNotableAlumni, Guignard]
-
A.
Giraud
Giraud is a French surname borne by various notable figures in French history and culture.
-
B.
Moreau
Moreau is a town in Saratoga County, New York, known for its residential communities and proximity to natural and recreational areas such as Moreau Lake State Park.
-
C.
Bézu Fache
Bézu Fache is the stern and devout captain of the French Judicial Police who leads the investigation at the Louvre in Dan Brown’s novel *The Da Vinci Code*.
-
D.
Honoré
Honoré is the given name of the renowned 19th-century French novelist and playwright Honoré de Balzac.
-
E.
Sauvestre
Sauvestre is a French surname most notably associated with architect Stephen Sauvestre, who contributed to the design of the Eiffel Tower.
- 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: Guignard Triple: [Escola Nacional de Belas Artes, hasNotableAlumni, Guignard]
Generated description
Guignard was a prominent Brazilian painter and art educator known for his lyrical landscapes and significant influence on modern Brazilian art.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Guignard Target entity description: Guignard was a prominent Brazilian painter and art educator known for his lyrical landscapes and significant influence on modern Brazilian art.
-
A.
Giraud
Giraud is a French surname borne by various notable figures in French history and culture.
-
B.
Moreau
Moreau is a town in Saratoga County, New York, known for its residential communities and proximity to natural and recreational areas such as Moreau Lake State Park.
-
C.
Bézu Fache
Bézu Fache is the stern and devout captain of the French Judicial Police who leads the investigation at the Louvre in Dan Brown’s novel *The Da Vinci Code*.
-
D.
Honoré
Honoré is the given name of the renowned 19th-century French novelist and playwright Honoré de Balzac.
-
E.
Sauvestre
Sauvestre is a French surname most notably associated with architect Stephen Sauvestre, who contributed to the design of the Eiffel Tower.
- 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_69a494082a7c819095004f423f294a64 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bce972cc8190bce0b77cfda6da41 |
completed | March 1, 2026, 10:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac8311ba6481908aaca4c1e9d8b78f |
completed | March 7, 2026, 7:57 p.m. |
| NEDg | Description generation | batch_69ac83add3608190be198ba153721d5c |
completed | March 7, 2026, 7:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac846e724081909696c8c44f2f9500 |
completed | March 7, 2026, 8:02 p.m. |
Created at: March 1, 2026, 7:45 p.m.