Triple
T19868239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legally Blonde |
E477445
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Anita Brandt-Burgoyne |
—
|
NE NERFINISHED |
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: Anita Brandt-Burgoyne | Statement: [Legally Blonde, editedBy, Anita Brandt-Burgoyne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anita Brandt-Burgoyne Context triple: [Legally Blonde, editedBy, Anita Brandt-Burgoyne]
-
A.
Anita Brandt-Burgoyne
chosen
Anita Brandt-Burgoyne is a film editor known for her work on feature films including the comedy "Dude, Where's My Car?".
-
B.
Karen Whitford
Karen Whitford is best known as the wife of Aerosmith guitarist Brad Whitford.
-
C.
Penny D'Amato
Penny D'Amato is best known as the former wife of longtime New York U.S. Senator Alfonse D'Amato.
-
D.
Kira Snyder
Kira Snyder is an American television and film writer known for her work on projects such as Pacific Rim: Uprising and the acclaimed series The Handmaid’s Tale.
-
E.
Connie Mills
Connie Mills is a small-town West Virginia police officer who becomes entangled in a series of eerie, prophetic events in the supernatural thriller "The Mothman Prophecies."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51e7d948190aedbcd6c30361c39 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e658a168288190a2fbb735d1fd30a8 |
completed | April 20, 2026, 4:47 p.m. |
Created at: April 10, 2026, 1:51 p.m.