Triple
T4043682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Drop Dead Diva |
E84011
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Kate Levering
Kate Levering is an American actress best known for her role as the driven attorney Kim Kaswell on the television series "Drop Dead Diva."
|
E525727
|
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: Kate Levering | Statement: [Drop Dead Diva, starring, Kate Levering]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Levering Context triple: [Drop Dead Diva, starring, Kate Levering]
-
A.
Karen Lewis
Karen Lewis is a television producer known for her work on the British drama series "Years and Years."
-
B.
Kathryn Morris
Kathryn Morris is an American actress best known for her lead role as Detective Lilly Rush on the television series "Cold Case."
-
C.
Mary Cunningham
Mary Cunningham is known as the spouse of Welsh actor Clive Merrison, recognized for his extensive work in British television, film, and radio drama.
-
D.
Maryanne Vollers
Maryanne Vollers is an American author, journalist, and ghostwriter known for collaborating on high-profile political and human rights memoirs.
-
E.
Linda Banwell
Linda Banwell is best known as the wife of the late English actor and director Bob Hoskins.
- 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: Kate Levering Triple: [Drop Dead Diva, starring, Kate Levering]
Generated description
Kate Levering is an American actress best known for her role as the driven attorney Kim Kaswell on the television series "Drop Dead Diva."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kate Levering Target entity description: Kate Levering is an American actress best known for her role as the driven attorney Kim Kaswell on the television series "Drop Dead Diva."
-
A.
Karen Lewis
Karen Lewis is a television producer known for her work on the British drama series "Years and Years."
-
B.
Kathryn Morris
Kathryn Morris is an American actress best known for her lead role as Detective Lilly Rush on the television series "Cold Case."
-
C.
Mary Cunningham
Mary Cunningham is known as the spouse of Welsh actor Clive Merrison, recognized for his extensive work in British television, film, and radio drama.
-
D.
Maryanne Vollers
Maryanne Vollers is an American author, journalist, and ghostwriter known for collaborating on high-profile political and human rights memoirs.
-
E.
Linda Banwell
Linda Banwell is best known as the wife of the late English actor and director Bob Hoskins.
- 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_69aed930bd5c819083e7dcc14fc44f69 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb5d759c8190b61fbbe94ffe2bf7 |
completed | March 9, 2026, 4:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf9ad2f8e48190aac71e657a9e1197 |
completed | March 22, 2026, 7:31 a.m. |
| NEDg | Description generation | batch_69bf9b9dbabc81908ef4d25455616f76 |
completed | March 22, 2026, 7:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf9c45029c8190b9588a436d9804af |
completed | March 22, 2026, 7:37 a.m. |
Created at: March 9, 2026, 3:37 p.m.