Triple
T2047751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Hangover Part II |
E45492
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Dan Goldberg |
E226506
|
NE FINISHED |
How this triple was built (2 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 Part II, producer, Dan Goldberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Goldberg Context triple: [The Hangover Part II, producer, Dan Goldberg]
-
A.
Dan Goldberg
chosen
Dan Goldberg is a film producer best known for his work on major Hollywood comedies, including the hit movie "The Hangover."
-
B.
Andrew G. Myers
Andrew G. Myers is an American organic chemist renowned for his contributions to complex molecule synthesis and medicinal chemistry.
-
C.
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.
-
D.
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.
-
E.
Robert Griesemer
Robert Griesemer is a Swiss software engineer best known as one of the principal designers of the Go programming language at Google.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a8891948208190ab7898da21824c77 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb974e8488190887b840c2cb88b3a |
completed | March 7, 2026, 5:36 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae2003e9488190b54ff042c91d4a62 |
completed | March 9, 2026, 1:19 a.m. |
Created at: March 4, 2026, 7:39 p.m.