Triple
T7530238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Knots Landing |
E178002
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Michele Lee
Michele Lee is an American actress and singer best known for her long-running role as Karen MacKenzie on the prime-time soap opera "Knots Landing."
|
E669476
|
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: Michele Lee | Statement: [Knots Landing, starring, Michele Lee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michele Lee Context triple: [Knots Landing, starring, Michele Lee]
-
A.
Linda Cho
Linda Cho is a Tony Award–winning costume designer known for her work on major Broadway productions and other theatrical performances.
-
B.
Karen Kwan
Karen Kwan is an American figure skater and the older sister of Olympic medalist Michelle Kwan.
-
C.
Eileen Loo
Eileen Loo was the wife of renowned Chinese-American architect I. M. Pei and a supportive partner throughout his celebrated career.
-
D.
Vivian Lee
Vivian Lee is a prominent architect and key leader at the internationally renowned firm Richard Meier & Partners Architects.
-
E.
Margaret Chung
Margaret Chung was a pioneering Chinese American physician and surgeon, widely regarded as the first Chinese American woman doctor in the United States and known for her influential role in supporting U.S. military personnel during World War II.
- 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: Michele Lee Triple: [Knots Landing, starring, Michele Lee]
Generated description
Michele Lee is an American actress and singer best known for her long-running role as Karen MacKenzie on the prime-time soap opera "Knots Landing."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michele Lee Target entity description: Michele Lee is an American actress and singer best known for her long-running role as Karen MacKenzie on the prime-time soap opera "Knots Landing."
-
A.
Linda Cho
Linda Cho is a Tony Award–winning costume designer known for her work on major Broadway productions and other theatrical performances.
-
B.
Karen Kwan
Karen Kwan is an American figure skater and the older sister of Olympic medalist Michelle Kwan.
-
C.
Eileen Loo
Eileen Loo was the wife of renowned Chinese-American architect I. M. Pei and a supportive partner throughout his celebrated career.
-
D.
Vivian Lee
Vivian Lee is a prominent architect and key leader at the internationally renowned firm Richard Meier & Partners Architects.
-
E.
Margaret Chung
Margaret Chung was a pioneering Chinese American physician and surgeon, widely regarded as the first Chinese American woman doctor in the United States and known for her influential role in supporting U.S. military personnel during World War II.
- 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_69c69f2acdbc8190b5a8320168c1d0ba |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f81fbd4c8190b8ffedf1dbbb43aa |
completed | March 27, 2026, 9:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8463e08ac8190abd4d19b58067233 |
completed | March 28, 2026, 9:21 p.m. |
| NEDg | Description generation | batch_69c846b326088190b93a32c70bcc97ca |
completed | March 28, 2026, 9:22 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8479490688190bc56b5a21d779b18 |
completed | March 28, 2026, 9:26 p.m. |
Created at: March 27, 2026, 3:47 p.m.