Triple
T18933944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kim Kaswell |
E463189
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Kate Levering |
—
|
NE NERFINISHED |
How this triple was built (2 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: [Kim Kaswell, portrayedBy, Kate Levering]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Levering Context triple: [Kim Kaswell, portrayedBy, Kate Levering]
-
A.
Kate Levering
chosen
Kate Levering is an American actress best known for her role as the driven attorney Kim Kaswell on the television series "Drop Dead Diva."
-
B.
Karen Lewis
Karen Lewis is a film and television producer known for her work on the project "Exile."
-
C.
Karen Lewis
Karen Lewis is a television producer known for her work on the British drama series "Years and Years."
-
D.
Karen Lewis
Karen Lewis is a British television producer best known for her work on acclaimed drama series such as "Last Tango in Halifax."
-
E.
Kathleen Lloyd
Kathleen Lloyd is an American actress best known for her film and television work in the 1970s and 1980s, including prominent roles in Westerns and crime dramas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dcfec90481909e926be9767e5779 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d3e57e648190aa4d3b09e84d4d38 |
completed | April 20, 2026, 7:21 a.m. |
Created at: April 10, 2026, 11:59 a.m.