Triple
T18576569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benenson |
E454001
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Anna Benenson
Anna Benenson is a notable individual recognized as a prominent bearer of the Benenson surname.
|
E1336512
|
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: Anna Benenson | Statement: [Benenson, hasNotableBearer, Anna Benenson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Benenson Context triple: [Benenson, hasNotableBearer, Anna Benenson]
-
A.
Ann Rosener
Ann Rosener was an American photographer best known for her documentary images of home-front life and industry during World War II, particularly through her work for U.S. government agencies.
-
B.
Emma Berman
Emma Berman is an American voice actress best known for voicing Giulia Marcovaldo in Pixar's animated film "Luca."
-
C.
Marisa Berenson
Marisa Berenson is an American actress and former fashion model best known for her roles in films such as "Cabaret" and "Barry Lyndon."
-
D.
Elizabeth Kaye
Elizabeth Kaye was an English noblewoman best known as the wife of Francis North, 1st Earl of Guilford, a prominent 17th-century lawyer and statesman.
-
E.
Jane Belson
Jane Belson was a British barrister best known as the wife of author Douglas Adams.
- 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: Anna Benenson Triple: [Benenson, hasNotableBearer, Anna Benenson]
Generated description
Anna Benenson is a notable individual recognized as a prominent bearer of the Benenson surname.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anna Benenson Target entity description: Anna Benenson is a notable individual recognized as a prominent bearer of the Benenson surname.
-
A.
Ann Rosener
Ann Rosener was an American photographer best known for her documentary images of home-front life and industry during World War II, particularly through her work for U.S. government agencies.
-
B.
Emma Berman
Emma Berman is an American voice actress best known for voicing Giulia Marcovaldo in Pixar's animated film "Luca."
-
C.
Marisa Berenson
Marisa Berenson is an American actress and former fashion model best known for her roles in films such as "Cabaret" and "Barry Lyndon."
-
D.
Elizabeth Kaye
Elizabeth Kaye was an English noblewoman best known as the wife of Francis North, 1st Earl of Guilford, a prominent 17th-century lawyer and statesman.
-
E.
Jane Belson
Jane Belson was a British barrister best known as the wife of author Douglas Adams.
- 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_69d8d38974308190a9174430ef256b73 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e543cc94f081909b76c3d488ed5637 |
completed | April 19, 2026, 9:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a05170d98a88190a048feff7f0bc7a4 |
completed | May 14, 2026, 12:27 a.m. |
| NEDg | Description generation | batch_6a051ad23658819081f5169a0506fda6 |
completed | May 14, 2026, 12:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a051b9a071c8190a0e92848a3596acd |
completed | May 14, 2026, 12:47 a.m. |
Created at: April 10, 2026, 11:43 a.m.