Triple
T2330628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turning Red |
E48392
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Steve Bloom
Steve Bloom is a film editor best known for his work on the Pixar animated feature "Turning Red."
|
E257348
|
NE FINISHED |
How this triple was built (4 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: Steve Bloom | Statement: [Turning Red, editedBy, Steve Bloom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steve Bloom Context triple: [Turning Red, editedBy, Steve Bloom]
-
A.
Brian Bilello
Brian Bilello is an American soccer executive best known for leading Major League Soccer’s New England Revolution as the club’s president.
-
B.
Don Stevens
Don Stevens is a notable individual recognized for achievements significant enough to be distinguished from others sharing the surname Stevens.
-
C.
Mike Krieger
Mike Krieger is a Brazilian-American entrepreneur and software engineer best known as the co-founder and former CTO of the photo-sharing social media platform Instagram.
-
D.
John Bloom
John Bloom is a film editor best known for his Academy Award-winning work on movies such as "Gandhi" and his editing contributions to notable films including "Charlie Wilson's War."
-
E.
Jeff Fager
Jeff Fager is an American television producer best known for leading and shaping the long-running CBS news magazine program "60 Minutes."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Steve Bloom Triple: [Turning Red, editedBy, Steve Bloom]
Generated description
Steve Bloom is a film editor best known for his work on the Pixar animated feature "Turning Red."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Steve Bloom Target entity description: Steve Bloom is a film editor best known for his work on the Pixar animated feature "Turning Red."
-
A.
Brian Bilello
Brian Bilello is an American soccer executive best known for leading Major League Soccer’s New England Revolution as the club’s president.
-
B.
Don Stevens
Don Stevens is a notable individual recognized for achievements significant enough to be distinguished from others sharing the surname Stevens.
-
C.
Mike Krieger
Mike Krieger is a Brazilian-American entrepreneur and software engineer best known as the co-founder and former CTO of the photo-sharing social media platform Instagram.
-
D.
John Bloom
John Bloom is a film editor best known for his Academy Award-winning work on movies such as "Gandhi" and his editing contributions to notable films including "Charlie Wilson's War."
-
E.
Jeff Fager
Jeff Fager is an American television producer best known for leading and shaping the long-running CBS news magazine program "60 Minutes."
- F. None of above. chosen
Provenance (5 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_69a88aa308a88190b0b86c011fda7fce |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc669956881908b8d9784d6a06acf |
completed | March 7, 2026, 6:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae96185a1c8190a115588f8a5b92f7 |
completed | March 9, 2026, 9:42 a.m. |
| NEDg | Description generation | batch_69ae96ea1b6c81908e05ca78c5e07320 |
completed | March 9, 2026, 9:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae97a775008190bdda2f63810cd5e8 |
completed | March 9, 2026, 9:49 a.m. |
Created at: March 4, 2026, 7:50 p.m.