Triple
T20140345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Virginia |
E491145
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object |
Nancy Schreiber
Nancy Schreiber is an acclaimed American cinematographer known for her work in independent film and television and for being one of the first women honored with the ASC Presidents Award.
|
E1485444
|
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: Nancy Schreiber | Statement: [Miss Virginia, cinematographyBy, Nancy Schreiber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nancy Schreiber Context triple: [Miss Virginia, cinematographyBy, Nancy Schreiber]
-
A.
Nancy Schafer
Nancy Schafer is a film and television producer known for her work on independent and documentary projects.
-
B.
Nancy Wilner
Nancy Wilner is best known as the first wife of American actor Robert Culp, with whom she was married in the 1950s.
-
C.
Nancy Goodman
Nancy Goodman is an American diplomat, businesswoman, and philanthropist best known for founding the Susan G. Komen Breast Cancer Foundation.
-
D.
Nancy Gross
Nancy Gross was the wife of renowned American film director Howard Hawks.
-
E.
Nancy Blansky
Nancy Blansky is the central character of the 1970s American sitcom "Blansky's Beauties," portrayed as a seasoned Las Vegas showbiz professional managing a troupe of young performers.
- 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: Nancy Schreiber Triple: [Miss Virginia, cinematographyBy, Nancy Schreiber]
Generated description
Nancy Schreiber is an acclaimed American cinematographer known for her work in independent film and television and for being one of the first women honored with the ASC Presidents Award.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nancy Schreiber Target entity description: Nancy Schreiber is an acclaimed American cinematographer known for her work in independent film and television and for being one of the first women honored with the ASC Presidents Award.
-
A.
Nancy Schafer
Nancy Schafer is a film and television producer known for her work on independent and documentary projects.
-
B.
Nancy Wilner
Nancy Wilner is best known as the first wife of American actor Robert Culp, with whom she was married in the 1950s.
-
C.
Nancy Goodman
Nancy Goodman is an American diplomat, businesswoman, and philanthropist best known for founding the Susan G. Komen Breast Cancer Foundation.
-
D.
Nancy Gross
Nancy Gross was the wife of renowned American film director Howard Hawks.
-
E.
Nancy Blansky
Nancy Blansky is the central character of the 1970s American sitcom "Blansky's Beauties," portrayed as a seasoned Las Vegas showbiz professional managing a troupe of young performers.
- 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_69da6265f8f0819080b29c752a574088 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66798d59c81908ebcd6644b1b3744 |
completed | April 20, 2026, 5:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09c8e823608190b996eaeb48838aed |
completed | May 17, 2026, 1:55 p.m. |
| NEDg | Description generation | batch_6a09cacf98f08190b8c7348e92e1bdc7 |
completed | May 17, 2026, 2:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09cb491e3c81908d369f66507dd809 |
completed | May 17, 2026, 2:06 p.m. |
Created at: April 11, 2026, 11:32 p.m.