Triple
T3065857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macbeth (2015 film) |
E62102
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Jacob Koskoff
Jacob Koskoff is an American screenwriter best known for co-writing the 2015 film adaptation of Shakespeare’s "Macbeth."
|
E356547
|
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: Jacob Koskoff | Statement: [Macbeth (2015 film), screenwriter, Jacob Koskoff]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jacob Koskoff Context triple: [Macbeth (2015 film), screenwriter, Jacob Koskoff]
-
A.
Marc Roskin
Marc Roskin is a television producer and director best known for his work on genre and adventure series such as "The Librarians."
-
B.
Michael Kagan
Michael Kagan is an Israeli technologist and entrepreneur best known as the co-founder and longtime chief technology officer of high-performance networking company Mellanox Technologies.
-
C.
Andrew Mondshein
Andrew Mondshein is an American film editor known for his work on acclaimed movies such as "Ma Rainey's Black Bottom" and "The Sixth Sense."
-
D.
Ali Weinberg
Ali Weinberg is an American journalist and television news producer known for her work covering politics for major U.S. news networks.
-
E.
Jake Cohen
Jake Cohen is known primarily as the son of Michael Cohen, the former personal attorney to Donald Trump.
- 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: Jacob Koskoff Triple: [Macbeth (2015 film), screenwriter, Jacob Koskoff]
Generated description
Jacob Koskoff is an American screenwriter best known for co-writing the 2015 film adaptation of Shakespeare’s "Macbeth."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jacob Koskoff Target entity description: Jacob Koskoff is an American screenwriter best known for co-writing the 2015 film adaptation of Shakespeare’s "Macbeth."
-
A.
Marc Roskin
Marc Roskin is a television producer and director best known for his work on genre and adventure series such as "The Librarians."
-
B.
Michael Kagan
Michael Kagan is an Israeli technologist and entrepreneur best known as the co-founder and longtime chief technology officer of high-performance networking company Mellanox Technologies.
-
C.
Andrew Mondshein
Andrew Mondshein is an American film editor known for his work on acclaimed movies such as "Ma Rainey's Black Bottom" and "The Sixth Sense."
-
D.
Ali Weinberg
Ali Weinberg is an American journalist and television news producer known for her work covering politics for major U.S. news networks.
-
E.
Jake Cohen
Jake Cohen is known primarily as the son of Michael Cohen, the former personal attorney to Donald Trump.
- 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_69ad85793e5c8190a358049bc4a98d8c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada0fc01dc81908fbdf7c1ef73afe4 |
completed | March 8, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b354528b00819095fa9b8f28f2135c |
completed | March 13, 2026, 12:03 a.m. |
| NEDg | Description generation | batch_69b35585fbd08190966c1263c165daa9 |
completed | March 13, 2026, 12:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3566da174819086160cd254e0a443 |
completed | March 13, 2026, 12:12 a.m. |
Created at: March 8, 2026, 3:02 p.m.