Triple
T11291851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Hangover trilogy |
E267343
|
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 trilogy, producer, Dan Goldberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Goldberg Context triple: [The Hangover trilogy, 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.
Dana Goldberg
Dana Goldberg is a film producer known for her work on major Hollywood blockbusters, including entries in the Terminator franchise.
-
C.
David Weinberg
David Weinberg is a name shared by multiple notable individuals, including professionals in fields such as science, academia, and the arts.
-
D.
Daniel Goldberg
Daniel Goldberg is a Canadian film producer best known for his long-running collaboration with Ivan Reitman on comedies such as "Meatballs," "Stripes," and "Old School."
-
E.
L. Peter Deutsch
L. Peter Deutsch is a computer scientist and software developer best known for creating the Ghostscript interpreter for the PostScript language and PDF files.
- 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_69d6aac993a08190a6f36445ebaf9a43 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e989fdac81909a4a75f1f68b55c6 |
completed | April 9, 2026, 6:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e50a246a3c81909f4f1d32a1b1efeb |
completed | April 19, 2026, 5 p.m. |
Created at: April 8, 2026, 9:32 p.m.