Triple
T10776649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Take the Money and Run |
E254213
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Jacquelyn Hyde
Jacquelyn Hyde is an actress best known for her role in Woody Allen’s 1969 comedy film "Take the Money and Run."
|
E944013
|
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: Jacquelyn Hyde | Statement: [Take the Money and Run, starring, Jacquelyn Hyde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jacquelyn Hyde Context triple: [Take the Money and Run, starring, Jacquelyn Hyde]
-
A.
Michelle Mylett
Michelle Mylett is a Canadian actress best known for playing Katy on the comedy series "Letterkenny."
-
B.
Karen Moss
Karen Moss is the biological mother of television personality and fashion designer Nicole Richie.
-
C.
Heather Faulkiner
Heather Faulkiner is known as the wife of prominent American sportscaster Marv Albert.
-
D.
Bridget Hyde
Bridget Hyde was an English noblewoman and heiress of the late 17th and early 18th centuries, notable for her substantial inherited estates and connections to prominent aristocratic families.
-
E.
Kathryn Alexander
Kathryn Alexander is known as the daughter of American politician and former U.S. Senator Lamar Alexander.
- 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: Jacquelyn Hyde Triple: [Take the Money and Run, starring, Jacquelyn Hyde]
Generated description
Jacquelyn Hyde is an actress best known for her role in Woody Allen’s 1969 comedy film "Take the Money and Run."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jacquelyn Hyde Target entity description: Jacquelyn Hyde is an actress best known for her role in Woody Allen’s 1969 comedy film "Take the Money and Run."
-
A.
Michelle Mylett
Michelle Mylett is a Canadian actress best known for playing Katy on the comedy series "Letterkenny."
-
B.
Karen Moss
Karen Moss is the biological mother of television personality and fashion designer Nicole Richie.
-
C.
Heather Faulkiner
Heather Faulkiner is known as the wife of prominent American sportscaster Marv Albert.
-
D.
Bridget Hyde
Bridget Hyde was an English noblewoman and heiress of the late 17th and early 18th centuries, notable for her substantial inherited estates and connections to prominent aristocratic families.
-
E.
Kathryn Alexander
Kathryn Alexander is known as the daughter of American politician and former U.S. Senator Lamar Alexander.
- 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_69d6aa609f008190a294200aefcb7bd5 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7329d8c908190bddad40685133ea1 |
completed | April 9, 2026, 5:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f018324bf88190bcd2bf168b1065d3 |
completed | April 28, 2026, 2:15 a.m. |
| NEDg | Description generation | batch_69f01d7ab930819095eaae226ab55b80 |
completed | April 28, 2026, 2:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f043ddbfe481908e0c439dbd3e944f |
completed | April 28, 2026, 5:21 a.m. |
Created at: April 8, 2026, 9:16 p.m.