Triple
T8509603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rrose Sélavy |
E201417
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
Rose Sélavy
Rose Sélavy is a pseudonym adopted by artist Marcel Duchamp, under which he explored themes of identity, gender, and artistic alter egos in his work.
|
E738416
|
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: Rose Sélavy | Statement: [Rrose Sélavy, alsoKnownAs, Rose Sélavy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rose Sélavy Context triple: [Rrose Sélavy, alsoKnownAs, Rose Sélavy]
-
A.
Capucine
Capucine was a French fashion model and film actress best known for her elegant screen presence in 1960s comedies and dramas, including roles in films like The Pink Panther.
-
B.
Fleur
Fleur is a feminine given name of French origin meaning "flower," often used as a middle name in English-speaking countries.
-
C.
Rose Dione
Rose Dione was a French-born actress active in early American silent cinema, known for character roles in films such as the 1921 adaptation of "Little Lord Fauntleroy."
-
D.
Rosier
Rosier is a surname of French origin borne by various notable individuals across different fields.
-
E.
Zibelle
Zibelle is a village in eastern Germany, historically part of Lusatia, known in this context as the place where physicist Walther Nernst died.
- 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: Rose Sélavy Triple: [Rrose Sélavy, alsoKnownAs, Rose Sélavy]
Generated description
Rose Sélavy is a pseudonym adopted by artist Marcel Duchamp, under which he explored themes of identity, gender, and artistic alter egos in his work.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rose Sélavy Target entity description: Rose Sélavy is a pseudonym adopted by artist Marcel Duchamp, under which he explored themes of identity, gender, and artistic alter egos in his work.
-
A.
Capucine
Capucine was a French fashion model and film actress best known for her elegant screen presence in 1960s comedies and dramas, including roles in films like The Pink Panther.
-
B.
Fleur
Fleur is a feminine given name of French origin meaning "flower," often used as a middle name in English-speaking countries.
-
C.
Rose Dione
Rose Dione was a French-born actress active in early American silent cinema, known for character roles in films such as the 1921 adaptation of "Little Lord Fauntleroy."
-
D.
Rosier
Rosier is a surname of French origin borne by various notable individuals across different fields.
-
E.
Zibelle
Zibelle is a village in eastern Germany, historically part of Lusatia, known in this context as the place where physicist Walther Nernst died.
- 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_69ca8320e5748190ac2c585a0bba8193 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe5e0cb1881909d1ff6ee9b3a65cc |
completed | March 31, 2026, 3:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce4e3faa0c81908533e9097ed29b26 |
completed | April 2, 2026, 11:08 a.m. |
| NEDg | Description generation | batch_69ce4ff9792c81908d76141904a170bc |
completed | April 2, 2026, 11:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce50acae5881908f1d2c0e21dd7f1d |
completed | April 2, 2026, 11:19 a.m. |
Created at: March 30, 2026, 6:15 p.m.