Triple
T3786673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminator Genisys |
E85545
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Dana Goldberg
Dana Goldberg is a film producer known for her work on major Hollywood blockbusters, including entries in the Terminator franchise.
|
E388283
|
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: Dana Goldberg | Statement: [Terminator Genisys, producer, Dana Goldberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dana Goldberg Context triple: [Terminator Genisys, producer, Dana Goldberg]
-
A.
Dan Goldberg
Dan Goldberg is a film producer best known for his work on major Hollywood comedies, including the hit movie "The Hangover."
-
B.
David Heitner
David Heitner is a film editor known for his work on the South African musical drama film "Sarafina!".
-
C.
Daniel Bobker
Daniel Bobker is a film producer known for his work on genre and fantasy projects, including the movie "The Brothers Grimm."
-
D.
Josh Goldstein
Josh Goldstein is a screenwriter best known for co-writing the story for Disney’s adventure film "Jungle Cruise."
-
E.
Jeremy Shamos
Jeremy Shamos is an American stage and screen actor known for his work on Broadway and in film and television.
- 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: Dana Goldberg Triple: [Terminator Genisys, producer, Dana Goldberg]
Generated description
Dana Goldberg is a film producer known for her work on major Hollywood blockbusters, including entries in the Terminator franchise.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dana Goldberg Target entity description: Dana Goldberg is a film producer known for her work on major Hollywood blockbusters, including entries in the Terminator franchise.
-
A.
Dan Goldberg
Dan Goldberg is a film producer best known for his work on major Hollywood comedies, including the hit movie "The Hangover."
-
B.
David Heitner
David Heitner is a film editor known for his work on the South African musical drama film "Sarafina!".
-
C.
Daniel Bobker
Daniel Bobker is a film producer known for his work on genre and fantasy projects, including the movie "The Brothers Grimm."
-
D.
Josh Goldstein
Josh Goldstein is a screenwriter best known for co-writing the story for Disney’s adventure film "Jungle Cruise."
-
E.
Jeremy Shamos
Jeremy Shamos is an American stage and screen actor known for his work on Broadway and in film and television.
- 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_69aed937fa8881908208ef3801060826 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee42d2bc88190ab85529fcd1aa20a |
completed | March 9, 2026, 3:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4f04ceea881909c3f0d1da9b538ae |
completed | March 14, 2026, 5:21 a.m. |
| NEDg | Description generation | batch_69b4f1730bbc8190a2f5f70ebc5a528a |
completed | March 14, 2026, 5:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4f2538eac81908a6c85ddab4e2356 |
completed | March 14, 2026, 5:29 a.m. |
Created at: March 9, 2026, 3:13 p.m.