Triple
T2461555
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | While We're Young |
E54544
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Maria Dizzia
Maria Dizzia is an American actress known for her work in film, television, and theater, including roles in projects like "Orange Is the New Black" and various independent films.
|
E268529
|
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: Maria Dizzia | Statement: [While We're Young, castMember, Maria Dizzia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maria Dizzia Context triple: [While We're Young, castMember, Maria Dizzia]
-
A.
Rebecca Giblin
Rebecca Giblin is an Australian legal scholar and advocate specializing in copyright, technology, and creators’ rights, known for her work on how digital platforms affect cultural industries.
-
B.
Jessica Barth
Jessica Barth is an American actress best known for playing Tami-Lynn in the comedy films "Ted" and "Ted 2."
-
C.
Pamela Frank
Pamela Frank is an acclaimed American violinist renowned for her expressive performances and influential teaching career.
-
D.
Molly Smith
Molly Smith is a daughter of FedEx founder and CEO Frederick W. Smith.
-
E.
Molly Smith
Molly Smith is an American film producer known for her work on acclaimed movies such as the crime thriller "Sicario."
- 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: Maria Dizzia Triple: [While We're Young, castMember, Maria Dizzia]
Generated description
Maria Dizzia is an American actress known for her work in film, television, and theater, including roles in projects like "Orange Is the New Black" and various independent films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maria Dizzia Target entity description: Maria Dizzia is an American actress known for her work in film, television, and theater, including roles in projects like "Orange Is the New Black" and various independent films.
-
A.
Rebecca Giblin
Rebecca Giblin is an Australian legal scholar and advocate specializing in copyright, technology, and creators’ rights, known for her work on how digital platforms affect cultural industries.
-
B.
Jessica Barth
Jessica Barth is an American actress best known for playing Tami-Lynn in the comedy films "Ted" and "Ted 2."
-
C.
Pamela Frank
Pamela Frank is an acclaimed American violinist renowned for her expressive performances and influential teaching career.
-
D.
Pamela Frank
Pamela Frank is the second wife of singer and civil rights activist Harry Belafonte, known primarily for her long-term marriage to the entertainer.
-
E.
Molly Smith
Molly Smith is a daughter of FedEx founder and CEO Frederick W. Smith.
- 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_69ab49dee84c819096b50a0049c347ac |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd11c47408190b10c7f6a151f2db2 |
completed | March 7, 2026, 7:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aef0d2b2748190b12611863d8bf4ad |
completed | March 9, 2026, 4:09 p.m. |
| NEDg | Description generation | batch_69aef3f1fb9481909748308457e6e3df |
completed | March 9, 2026, 4:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69aef8398fd08190821e9503c01af80a |
completed | March 9, 2026, 4:41 p.m. |
Created at: March 6, 2026, 9:44 p.m.