Triple
T5600979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Outpost |
E147117
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Jeffrey Greenstein
Jeffrey Greenstein is a film producer known for his work on action and genre movies, including the war drama "The Outpost."
|
E545858
|
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: Jeffrey Greenstein | Statement: [The Outpost, producer, Jeffrey Greenstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeffrey Greenstein Context triple: [The Outpost, producer, Jeffrey Greenstein]
-
A.
Michael Greenberg
Michael Greenberg is a prominent American neuroscientist renowned for his pioneering work on activity-dependent gene expression in the brain.
-
B.
Marc Greenberg
Marc Greenberg is a film producer known for his work on the Pixar short film "The Blue Umbrella."
-
C.
Dan Greenburg
Dan Greenburg is an American author and humorist best known for his satirical books and children's series such as "The Zack Files."
-
D.
Steven J. Green
Steven J. Green is an American businessman, philanthropist, and former U.S. ambassador whose support for education and international affairs led to a major public policy school being named in his honor.
-
E.
Benjamin Green
Benjamin Green was a 19th-century British architect best known for designing prominent public monuments and buildings in northern England.
- 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: Jeffrey Greenstein Triple: [The Outpost, producer, Jeffrey Greenstein]
Generated description
Jeffrey Greenstein is a film producer known for his work on action and genre movies, including the war drama "The Outpost."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jeffrey Greenstein Target entity description: Jeffrey Greenstein is a film producer known for his work on action and genre movies, including the war drama "The Outpost."
-
A.
Michael Greenberg
Michael Greenberg is a prominent American neuroscientist renowned for his pioneering work on activity-dependent gene expression in the brain.
-
B.
Marc Greenberg
Marc Greenberg is a film producer known for his work on the Pixar short film "The Blue Umbrella."
-
C.
Dan Greenburg
Dan Greenburg is an American author and humorist best known for his satirical books and children's series such as "The Zack Files."
-
D.
Steven J. Green
Steven J. Green is an American businessman, philanthropist, and former U.S. ambassador whose support for education and international affairs led to a major public policy school being named in his honor.
-
E.
Benjamin Green
Benjamin Green was a 19th-century British architect best known for designing prominent public monuments and buildings in northern England.
- 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_69c009043d648190a7af89698ccf1e3e |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020da519c81908626b243e40db263 |
completed | March 22, 2026, 5:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07d931c2c819081ee41a633436d7d |
completed | March 22, 2026, 11:38 p.m. |
| NEDg | Description generation | batch_69c08c6a7d10819084ec17299ec0df69 |
completed | March 23, 2026, 12:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c08cc71a9c8190ac3aa082cb7bf0fc |
completed | March 23, 2026, 12:43 a.m. |
Created at: March 22, 2026, 3:39 p.m.