Triple
T10465232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Funny Girl |
E246776
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Barbara Parker
Barbara Parker is the ambitious, quick-witted young woman who rises to stardom as a comedienne and singer in the musical "Funny Girl."
|
E992215
|
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: Barbara Parker | Statement: [Funny Girl, mainCharacter, Barbara Parker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barbara Parker Context triple: [Funny Girl, mainCharacter, Barbara Parker]
-
A.
Barbara Tucker
Barbara Tucker is an American house and dance music singer, songwriter, and choreographer known for her powerful vocals and influential club hits in the 1990s and 2000s.
-
B.
Barbara Morse
Barbara "Bobbi" Morse is a Marvel Comics character known as the superhero and S.H.I.E.L.D. agent Mockingbird.
-
C.
Barbara Richardson
Barbara Richardson is an American public figure best known as the longtime wife and partner of the late New Mexico governor and U.S. diplomat Bill Richardson, with whom she was active in civic and charitable causes.
-
D.
Barbara Leeds
Barbara Leeds was an American actress best known for her film and television work in the mid-20th century.
-
E.
Ruth Barrett
Ruth Barrett is a British composer best known for her evocative scores for film and television dramas.
- 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: Barbara Parker Triple: [Funny Girl, mainCharacter, Barbara Parker]
Generated description
Barbara Parker is the ambitious, quick-witted young woman who rises to stardom as a comedienne and singer in the musical "Funny Girl."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Barbara Parker Target entity description: Barbara Parker is the ambitious, quick-witted young woman who rises to stardom as a comedienne and singer in the musical "Funny Girl."
-
A.
Barbara Tucker
Barbara Tucker is an American house and dance music singer, songwriter, and choreographer known for her powerful vocals and influential club hits in the 1990s and 2000s.
-
B.
Barbara Morse
Barbara "Bobbi" Morse is a Marvel Comics character known as the superhero and S.H.I.E.L.D. agent Mockingbird.
-
C.
Barbara Richardson
Barbara Richardson is an American public figure best known as the longtime wife and partner of the late New Mexico governor and U.S. diplomat Bill Richardson, with whom she was active in civic and charitable causes.
-
D.
Barbara Leeds
Barbara Leeds was an American actress best known for her film and television work in the mid-20th century.
-
E.
Ruth Barrett
Ruth Barrett is a British composer best known for her evocative scores for film and television dramas.
- 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_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d50886c2a8819086da6c08356ec6bf |
completed | April 7, 2026, 1:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65e9136bc8190b35685376da7007e |
completed | May 2, 2026, 8:29 p.m. |
| NEDg | Description generation | batch_69f660bc541c8190a4d1d7a4cc959ecf |
completed | May 2, 2026, 8:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6617997188190bfce14c54619af7f |
completed | May 2, 2026, 8:41 p.m. |
Created at: April 6, 2026, 12:19 p.m.