Triple
T11982382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cinderella II: Dreams Come True |
E285192
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object |
Julie Rogers
Julie Rogers is a film editor known for her work on the animated sequel "Cinderella II: Dreams Come True."
|
E976387
|
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: Julie Rogers | Statement: [Cinderella II: Dreams Come True, editor, Julie Rogers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Julie Rogers Context triple: [Cinderella II: Dreams Come True, editor, Julie Rogers]
-
A.
Jill Eikenberry
Jill Eikenberry is an American actress best known for her Emmy-nominated role as attorney Ann Kelsey on the television series "L.A. Law."
-
B.
Barbara Luddy
Barbara Luddy was an American voice actress best known for her work in classic Disney animated films, including voicing the title character in "Lady and the Tramp."
-
C.
Kim Roberts
Kim Roberts is a film editor known for her work on the documentary "Waiting for Superman."
-
D.
Joan Allen
Joan Allen is an acclaimed American actress known for her versatile performances in film, television, and theater, including prominent roles in dramas and political thrillers.
-
E.
Peggy Dow
Peggy Dow is an American former film actress best known for her roles in early 1950s Hollywood movies, including the classic comedy "Harvey."
- 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: Julie Rogers Triple: [Cinderella II: Dreams Come True, editor, Julie Rogers]
Generated description
Julie Rogers is a film editor known for her work on the animated sequel "Cinderella II: Dreams Come True."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Julie Rogers Target entity description: Julie Rogers is a film editor known for her work on the animated sequel "Cinderella II: Dreams Come True."
-
A.
Jill Eikenberry
Jill Eikenberry is an American actress best known for her Emmy-nominated role as attorney Ann Kelsey on the television series "L.A. Law."
-
B.
Barbara Luddy
Barbara Luddy was an American voice actress best known for her work in classic Disney animated films, including voicing the title character in "Lady and the Tramp."
-
C.
Kim Roberts
Kim Roberts is a film editor known for her work on the documentary "Waiting for Superman."
-
D.
Joan Allen
Joan Allen is an acclaimed American actress known for her versatile performances in film, television, and theater, including prominent roles in dramas and political thrillers.
-
E.
Peggy Dow
Peggy Dow is an American former film actress best known for her roles in early 1950s Hollywood movies, including the classic comedy "Harvey."
- 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903973c848190aac871d6dfecc74b |
completed | April 10, 2026, 2:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e391d7c8190a414cb3306bfe139 |
completed | May 2, 2026, 3:54 p.m. |
| NEDg | Description generation | batch_69f622a646c481908164ae5387625bb4 |
completed | May 2, 2026, 4:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f623f5aa608190bce3e62e08077216 |
completed | May 2, 2026, 4:19 p.m. |
Created at: April 8, 2026, 9:46 p.m.