Triple
T3757762
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Wackness |
E82088
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Lauren Munsch
Lauren Munsch is a film producer best known for her work on the coming-of-age drama "The Wackness."
|
E385688
|
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: Lauren Munsch | Statement: [The Wackness, producer, Lauren Munsch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lauren Munsch Context triple: [The Wackness, producer, Lauren Munsch]
-
A.
Lane Smith
Lane Smith was an American character actor known for his roles in film and television, including portrayals of authoritative and often gruff figures.
-
B.
Jazmyn Simon
Jazmyn Simon is an American actress known for her roles on television series such as "Ballers" and "Psych: The Movie."
-
C.
Pamela Gray
Pamela Gray is an American screenwriter known for her work on character-driven drama films, including the military biographical film "Megan Leavey."
-
D.
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.
-
E.
Laura Shusterman
Laura Shusterman is the wife of former Donald Trump attorney Michael Cohen and a Ukrainian-born businesswoman who has been linked to some of his real estate and taxi-medallion ventures.
- 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: Lauren Munsch Triple: [The Wackness, producer, Lauren Munsch]
Generated description
Lauren Munsch is a film producer best known for her work on the coming-of-age drama "The Wackness."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lauren Munsch Target entity description: Lauren Munsch is a film producer best known for her work on the coming-of-age drama "The Wackness."
-
A.
Lane Smith
Lane Smith was an American character actor known for his roles in film and television, including portrayals of authoritative and often gruff figures.
-
B.
Jazmyn Simon
Jazmyn Simon is an American actress known for her roles on television series such as "Ballers" and "Psych: The Movie."
-
C.
Pamela Gray
Pamela Gray is an American screenwriter known for her work on character-driven drama films, including the military biographical film "Megan Leavey."
-
D.
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.
-
E.
Laura Shusterman
Laura Shusterman is the wife of former Donald Trump attorney Michael Cohen and a Ukrainian-born businesswoman who has been linked to some of his real estate and taxi-medallion ventures.
- 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbc04d348190b0e4a90d18bdd160 |
completed | March 8, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4e50f77fc8190b7774a7359118c9c |
completed | March 14, 2026, 4:33 a.m. |
| NEDg | Description generation | batch_69b4e5fe22f0819088effd8a0eae72e6 |
completed | March 14, 2026, 4:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4e671e02c819094cae2a3a2abb1b4 |
completed | March 14, 2026, 4:39 a.m. |
Created at: March 8, 2026, 3:35 p.m.