Triple
T3065932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Widows (2018 film) |
E62103
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Belle
Belle is a supporting character in the 2018 heist thriller film "Widows," involved in the criminal plot led by a group of women in Chicago.
|
E323620
|
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: Belle | Statement: [Widows (2018 film), character, Belle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belle Context triple: [Widows (2018 film), character, Belle]
-
A.
Belle
Belle is the intelligent, book-loving heroine of Disney’s "Beauty and the Beast," known for her compassion, independence, and iconic yellow ball gown.
-
B.
Belle Bennett
Belle Bennett was an American stage and silent film actress best known for her emotionally powerful performances in early 20th-century cinema.
-
C.
Tiana
Tiana is a Disney Princess known for her hardworking, ambitious nature and role as the first African-American princess in Disney’s animated film "The Princess and the Frog."
-
D.
Drizella Tremaine
Drizella Tremaine is one of Cinderella’s vain and spiteful stepsisters in Disney’s Cinderella, known for her jealousy, cruelty, and comic incompetence.
-
E.
Louise
Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
- 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: Belle Triple: [Widows (2018 film), character, Belle]
Generated description
Belle is a supporting character in the 2018 heist thriller film "Widows," involved in the criminal plot led by a group of women in Chicago.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Belle Target entity description: Belle is a supporting character in the 2018 heist thriller film "Widows," involved in the criminal plot led by a group of women in Chicago.
-
A.
Belle
Belle is the intelligent, book-loving heroine of Disney’s "Beauty and the Beast," known for her compassion, independence, and iconic yellow ball gown.
-
B.
Belle Bennett
Belle Bennett was an American stage and silent film actress best known for her emotionally powerful performances in early 20th-century cinema.
-
C.
Tiana
Tiana is a Disney Princess known for her hardworking, ambitious nature and role as the first African-American princess in Disney’s animated film "The Princess and the Frog."
-
D.
Drizella Tremaine
Drizella Tremaine is one of Cinderella’s vain and spiteful stepsisters in Disney’s Cinderella, known for her jealousy, cruelty, and comic incompetence.
-
E.
Louise
Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
- 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_69ad85793e5c8190a358049bc4a98d8c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada0fc01dc81908fbdf7c1ef73afe4 |
completed | March 8, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1ef1402108190a2d24e7eb523f658 |
completed | March 11, 2026, 10:39 p.m. |
| NEDg | Description generation | batch_69b1f2f3d120819090d28e0353d3d8da |
completed | March 11, 2026, 10:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b1f365a4988190ae3ea6370a27ee72 |
completed | March 11, 2026, 10:57 p.m. |
Created at: March 8, 2026, 3:02 p.m.