Triple
T7357051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suits |
E169651
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object |
Sarah Rafferty
Sarah Rafferty is an American actress best known for playing the sharp-witted legal secretary Donna Paulsen on the television series "Suits."
|
E683914
|
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 Rafferty | Statement: [Suits, portrayedBy, Sarah Rafferty]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sarah Rafferty Context triple: [Suits, portrayedBy, Sarah Rafferty]
-
A.
Rebecca McGuinness
Rebecca McGuinness is known as the wife of renowned English motorcycle road racer John McGuinness.
-
B.
Sarah O'Connell
Sarah O'Connell is the wife of British-Australian radio presenter and comedian Christian O'Connell.
-
C.
Heather O’Rourke
Heather O’Rourke was an American child actress best known for her role as Carol Anne Freeling in the "Poltergeist" film series.
-
D.
Sarah O’Meara
Sarah O’Meara is known as the spouse of Australian film director Paul Cox.
-
E.
Siobhan Hartnett
Siobhan Hartnett is an individual notable enough to be recognized as a bearer of the Hartnett surname, though specific public details about her life or achievements are not widely documented.
- 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 Rafferty Triple: [Suits, portrayedBy, Sarah Rafferty]
Generated description
Sarah Rafferty is an American actress best known for playing the sharp-witted legal secretary Donna Paulsen on the television series "Suits."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sarah Rafferty Target entity description: Sarah Rafferty is an American actress best known for playing the sharp-witted legal secretary Donna Paulsen on the television series "Suits."
-
A.
Rebecca McGuinness
Rebecca McGuinness is known as the wife of renowned English motorcycle road racer John McGuinness.
-
B.
Sarah O'Connell
Sarah O'Connell is the wife of British-Australian radio presenter and comedian Christian O'Connell.
-
C.
Heather O’Rourke
Heather O’Rourke was an American child actress best known for her role as Carol Anne Freeling in the "Poltergeist" film series.
-
D.
Sarah O’Meara
Sarah O’Meara is known as the spouse of Australian film director Paul Cox.
-
E.
Siobhan Hartnett
Siobhan Hartnett is an individual notable enough to be recognized as a bearer of the Hartnett surname, though specific public details about her life or achievements are not widely documented.
- 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_69c68a59f2288190877ca15c19b1e822 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f13a62e48190a2d1781a630aa9f0 |
completed | March 27, 2026, 9:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b4e3181481909eec1a09ae295923 |
completed | March 29, 2026, 5:13 a.m. |
| NEDg | Description generation | batch_69c8b61e0c308190b3231fab20bad278 |
completed | March 29, 2026, 5:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8b675ddb0819085d79dbba560d08f |
completed | March 29, 2026, 5:19 a.m. |
Created at: March 27, 2026, 3:06 p.m.