Triple
T10495369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boomers |
E247524
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
Paula Wilcox
Paula Wilcox is an English actress best known for her extensive work in British television sitcoms and dramas since the 1970s.
|
E980697
|
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: Paula Wilcox | Statement: [Boomers, stars, Paula Wilcox]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paula Wilcox Context triple: [Boomers, stars, Paula Wilcox]
-
A.
Paula Cox
Paula Cox is a Bermudian politician and lawyer who served as the first female Premier of Bermuda and leader of the Progressive Labour Party.
-
B.
Paula Hart
Paula Hart is a television producer best known for her work on family-oriented series and films, including overseeing the popular sitcom "Sabrina the Teenage Witch."
-
C.
Laura Bickford
Laura Bickford is an American film producer best known for her work on acclaimed independent and studio films, including the Oscar-winning drama "Traffic."
-
D.
Pam Williams
Pam Williams is a film producer best known for her work on the critically acclaimed historical drama "The Butler."
-
E.
Paula McGee
Paula McGee is a former American women's basketball standout best known for her starring collegiate career with the powerhouse USC Trojans in the early 1980s.
- 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: Paula Wilcox Triple: [Boomers, stars, Paula Wilcox]
Generated description
Paula Wilcox is an English actress best known for her extensive work in British television sitcoms and dramas since the 1970s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Paula Wilcox Target entity description: Paula Wilcox is an English actress best known for her extensive work in British television sitcoms and dramas since the 1970s.
-
A.
Paula Cox
Paula Cox is a Bermudian politician and lawyer who served as the first female Premier of Bermuda and leader of the Progressive Labour Party.
-
B.
Paula Hart
Paula Hart is a television producer best known for her work on family-oriented series and films, including overseeing the popular sitcom "Sabrina the Teenage Witch."
-
C.
Laura Bickford
Laura Bickford is an American film producer best known for her work on acclaimed independent and studio films, including the Oscar-winning drama "Traffic."
-
D.
Pam Williams
Pam Williams is a film producer best known for her work on the critically acclaimed historical drama "The Butler."
-
E.
Paula McGee
Paula McGee is a former American women's basketball standout best known for her starring collegiate career with the powerhouse USC Trojans in the early 1980s.
- 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5098be488819083d614f528cd82fb |
completed | April 7, 2026, 1:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63449892881909d361815cbfcdee5 |
completed | May 2, 2026, 5:28 p.m. |
| NEDg | Description generation | batch_69f635997b088190b6207fcac5594eb2 |
completed | May 2, 2026, 5:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f636d727a08190882eec3fd664b64d |
completed | May 2, 2026, 5:39 p.m. |
Created at: April 6, 2026, 12:24 p.m.