Triple
T1987352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Hangover |
E43171
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Dan Goldberg
Dan Goldberg is a film producer best known for his work on major Hollywood comedies, including the hit movie "The Hangover."
|
E226506
|
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: Dan Goldberg | Statement: [The Hangover, producer, Dan Goldberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Goldberg Context triple: [The Hangover, producer, Dan Goldberg]
-
A.
Andrew G. Myers
Andrew G. Myers is an American organic chemist renowned for his contributions to complex molecule synthesis and medicinal chemistry.
-
B.
Jonathan Goldstein
Jonathan Goldstein is an American screenwriter and filmmaker best known for co-writing hit studio comedies such as Horrible Bosses and Spider-Man: Homecoming.
-
C.
Hal Abelson
Hal Abelson is an American computer scientist and MIT professor known for his pioneering work in computer science education, open knowledge, and software freedom.
-
D.
Robert Griesemer
Robert Griesemer is a Swiss software engineer best known as one of the principal designers of the Go programming language at Google.
-
E.
Oren Patashnik
Oren Patashnik is a computer scientist best known for coauthoring the influential textbook "Concrete Mathematics" and for creating the BibTeX reference management tool used with LaTeX.
- 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: Dan Goldberg Triple: [The Hangover, producer, Dan Goldberg]
Generated description
Dan Goldberg is a film producer best known for his work on major Hollywood comedies, including the hit movie "The Hangover."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dan Goldberg Target entity description: Dan Goldberg is a film producer best known for his work on major Hollywood comedies, including the hit movie "The Hangover."
-
A.
Andrew G. Myers
Andrew G. Myers is an American organic chemist renowned for his contributions to complex molecule synthesis and medicinal chemistry.
-
B.
Jonathan Goldstein
Jonathan Goldstein is an American screenwriter and filmmaker best known for co-writing hit studio comedies such as Horrible Bosses and Spider-Man: Homecoming.
-
C.
Hal Abelson
Hal Abelson is an American computer scientist and MIT professor known for his pioneering work in computer science education, open knowledge, and software freedom.
-
D.
Robert Griesemer
Robert Griesemer is a Swiss software engineer best known as one of the principal designers of the Go programming language at Google.
-
E.
Oren Patashnik
Oren Patashnik is a computer scientist best known for coauthoring the influential textbook "Concrete Mathematics" and for creating the BibTeX reference management tool used with LaTeX.
- 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_69a88713ddc88190a969715658ebe7a8 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb840a5708190a9b64564b855fb22 |
completed | March 7, 2026, 5:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae0ad53ccc8190b0e0f44cfddfe9a4 |
completed | March 8, 2026, 11:48 p.m. |
| NEDg | Description generation | batch_69ae0b49abfc81908876ea54c7b7dcc2 |
completed | March 8, 2026, 11:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae0d1bb5c881908c27bdd359e78773 |
completed | March 8, 2026, 11:58 p.m. |
Created at: March 4, 2026, 7:37 p.m.