Triple
T11253828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shelley Berman |
E266386
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Sarah Herman
Sarah Herman is best known as the wife of American comedian and actor Shelley Berman.
|
E918583
|
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: Sarah Herman | Statement: [Shelley Berman, spouse, Sarah Herman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sarah Herman Context triple: [Shelley Berman, spouse, Sarah Herman]
-
A.
Sarah Hoadly
Sarah Hoadly was an English portrait painter of the early 18th century, known for her refined style and connections to prominent artistic circles in London.
-
B.
Sarah Packard
Sarah Packard is a troubled, emotionally fragile woman who becomes romantically involved with pool hustler "Fast" Eddie Felson in the 1961 film *The Hustler*.
-
C.
Sarah Snodgrass
Sarah Snodgrass is a person notable enough to be recognized as a bearer of the surname Snodgrass, though specific widely known public details about her are not clearly established.
-
D.
Sarah Greer
Sarah Greer is a British academic and higher education leader who serves as Vice-Chancellor of the University of Winchester.
-
E.
Elizabeth Reaser
Elizabeth Reaser is an American actress best known for her roles in the Twilight film series and the television drama Grey's Anatomy.
- 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: Sarah Herman Triple: [Shelley Berman, spouse, Sarah Herman]
Generated description
Sarah Herman is best known as the wife of American comedian and actor Shelley Berman.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sarah Herman Target entity description: Sarah Herman is best known as the wife of American comedian and actor Shelley Berman.
-
A.
Sarah Hoadly
Sarah Hoadly was an English portrait painter of the early 18th century, known for her refined style and connections to prominent artistic circles in London.
-
B.
Sarah Packard
Sarah Packard is a troubled, emotionally fragile woman who becomes romantically involved with pool hustler "Fast" Eddie Felson in the 1961 film *The Hustler*.
-
C.
Sarah Snodgrass
Sarah Snodgrass is a person notable enough to be recognized as a bearer of the surname Snodgrass, though specific widely known public details about her are not clearly established.
-
D.
Sarah Greer
Sarah Greer is a British academic and higher education leader who serves as Vice-Chancellor of the University of Winchester.
-
E.
Elizabeth Reaser
Elizabeth Reaser is an American actress best known for her roles in the Twilight film series and the television drama Grey's Anatomy.
- 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_69d6aac7953c8190b82caf9d7640fdf9 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9346f4c8190b29c2cf3a29cd1d1 |
completed | April 9, 2026, 6 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5257dd1e48190a9352fc8b62418da |
completed | April 19, 2026, 6:57 p.m. |
| NEDg | Description generation | batch_69e52a78951c8190923711067cf4e7e5 |
completed | April 19, 2026, 7:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e5319b6ef0819096debabfb6ffbe70 |
completed | April 19, 2026, 7:48 p.m. |
Created at: April 8, 2026, 9:31 p.m.