Triple
T9234280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Baroness and the Butler |
E221895
|
entity |
| Predicate | hasScreenwriter |
P62466
|
FINISHED |
| Object | Sam Hellman |
E818868
|
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: Sam Hellman | Statement: [The Baroness and the Butler, hasScreenwriter, Sam Hellman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sam Hellman Context triple: [The Baroness and the Butler, hasScreenwriter, Sam Hellman]
-
A.
Sam Hellman
chosen
Sam Hellman was an American screenwriter active during Hollywood’s studio era, known for contributing to numerous films including classic Westerns.
-
B.
Jason Hellmann
Jason Hellmann is a film editor known for his work on the survival thriller movie "The Grey."
-
C.
Guy Rothblum
Guy Rothblum is a theoretical computer scientist known for his work in cryptography and complexity theory.
-
D.
Marty Adelstein
Marty Adelstein is an American television producer and executive known for developing and producing numerous popular TV series.
-
E.
Steven Fierberg
Steven Fierberg is an American cinematographer known for his work on feature films and television series, including the romantic drama "Love & Other Drugs."
- 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_69ca83ed628c8190bc02d641e57f097f |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccee1baa3c8190870d1e850ccab1e0 |
completed | April 1, 2026, 10:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2696450e4819086d5aac368127e5f |
completed | April 5, 2026, 1:53 p.m. |
Created at: March 30, 2026, 7:29 p.m.