Triple
T8036669
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Gift |
E187124
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Bob Murawski |
E311866
|
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: Bob Murawski | Statement: [The Gift, editedBy, Bob Murawski]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bob Murawski Context triple: [The Gift, editedBy, Bob Murawski]
-
A.
Bob Murawski
chosen
Bob Murawski is an American film editor best known for his work on movies such as "The Hurt Locker," for which he won an Academy Award.
-
B.
Don Smolenski
Don Smolenski is an American sports executive best known for serving as the president of the NFL’s Philadelphia Eagles, overseeing the franchise’s business operations.
-
C.
Peter Melnick
Peter Melnick is an American composer known for his film and television scores, including his work on the romantic comedy "L.A. Story."
-
D.
Michael Vavitch
Michael Vavitch was a silent-era film actor known for his role in the 1924 drama "The Red Lily."
-
E.
Philip LaZebnik
Philip LaZebnik is an American screenwriter and playwright best known for his work on animated feature films such as Disney’s "Mulan" and "Pocahontas" and DreamWorks’ "The Prince of Egypt."
- 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_69ca82ae2d1081909dbfee42b41db419 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3f188e1c8190b92760c91d31f2df |
completed | March 31, 2026, 3:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd6759e83c8190869732f955279cee |
completed | April 1, 2026, 6:43 p.m. |
Created at: March 30, 2026, 5:22 p.m.