Triple
T21385347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Ann Lipton |
E527479
|
entity |
| Predicate | stageName |
P7872
|
FINISHED |
| Object | Peggy Lipton |
—
|
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: Peggy Lipton | Statement: [Margaret Ann Lipton, stageName, Peggy Lipton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peggy Lipton Context triple: [Margaret Ann Lipton, stageName, Peggy Lipton]
-
A.
Peggy Lipton
chosen
Peggy Lipton was an American actress and model best known for her Golden Globe–winning role as Julie Barnes on the 1960s TV series "The Mod Squad" and later as Norma Jennings on "Twin Peaks."
-
B.
Paula Prentiss
Paula Prentiss is an American actress known for her tall, comedic presence in 1960s films and television, including roles in movies like "Where the Boys Are" and "The Stepford Wives."
-
C.
Cheryl Ladd
Cheryl Ladd is
-
D.
Loretta Swit
Loretta Swit is an American actress best known for her Emmy-winning role as Major Margaret "Hot Lips" Houlihan on the television series M*A*S*H.
-
E.
Angie Dickinson
Angie Dickinson is an American actress best known for her roles in films like "Rio Bravo" and the TV series "Police Woman," which made her a prominent television star in the 1970s.
- 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_69e0b51f363c8190944000ab5523b02b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69ee62c9494081909efa74e189454dc6 |
completed | April 26, 2026, 7:08 p.m. |
Created at: April 16, 2026, 5:12 p.m.