Triple
T8020107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beautiful Girls |
E186717
|
entity |
| Predicate | screenplayBy |
P15305
|
FINISHED |
| Object | Scott Rosenberg |
E300554
|
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: Scott Rosenberg | Statement: [Beautiful Girls, screenplayBy, Scott Rosenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scott Rosenberg Context triple: [Beautiful Girls, screenplayBy, Scott Rosenberg]
-
A.
Scott Rosenberg
chosen
Scott Rosenberg is an American screenwriter and producer known for writing high-profile films such as "Con Air," "Gone in 60 Seconds," and "High Fidelity."
-
B.
Mark Rosenberg
Mark Rosenberg was an American film producer known for his work on notable movies of the 1980s and early 1990s.
-
C.
Dave Rosenberg
Dave Rosenberg is a technology entrepreneur best known as a co-founder of MuleSoft, a leading integration and API management platform company.
-
D.
Josh Kesselman
Josh Kesselman is a film and television producer best known for his work as an executive producer on projects such as the series "The Great."
-
E.
Jeff Kodosky
Jeff Kodosky is an American engineer and co-founder of National Instruments, best known as the "father of LabVIEW" for creating the influential graphical programming environment.
- 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_69ca82ac7fc081909b1398cf025423af |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3e8bc90081909f6f5878e6f1f241 |
completed | March 31, 2026, 3:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce38d1e5508190abf808fa06f89627 |
completed | April 2, 2026, 9:37 a.m. |
Created at: March 30, 2026, 5:20 p.m.