Triple
T2643949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Disney Princess franchise |
E62941
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
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.
|
E285208
|
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: [Disney Princess franchise, hasPart, Belle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belle Context triple: [Disney Princess franchise, hasPart, Belle]
-
A.
Belle Bennett
Belle Bennett was an American stage and silent film actress best known for her emotionally powerful performances in early 20th-century cinema.
-
B.
Louise
Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
-
C.
Rapunzel
Rapunzel is a classic fairy-tale princess best known for her extraordinarily long hair and her story of captivity in a tower and eventual escape.
-
D.
Elsa
Elsa is a feminine given name of Germanic origin, widely recognized today through its use for the main character in Disney's animated film "Frozen."
-
E.
Sylvie
Sylvie is a feminine given name, often used as a French variant of Sylvia, associated with meanings related to the forest or woods.
- 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: [Disney Princess franchise, hasPart, Belle]
Generated description
Belle is the intelligent, book-loving heroine of Disney’s "Beauty and the Beast," known for her compassion, independence, and iconic yellow ball gown.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Belle Target entity description: Belle is the intelligent, book-loving heroine of Disney’s "Beauty and the Beast," known for her compassion, independence, and iconic yellow ball gown.
-
A.
Belle Bennett
Belle Bennett was an American stage and silent film actress best known for her emotionally powerful performances in early 20th-century cinema.
-
B.
Louise
Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
-
C.
Rapunzel
Rapunzel is a classic fairy-tale princess best known for her extraordinarily long hair and her story of captivity in a tower and eventual escape.
-
D.
Elsa
Elsa is a feminine given name of Germanic origin, widely recognized today through its use for the main character in Disney's animated film "Frozen."
-
E.
Sylvie
Sylvie is a feminine given name, often used as a French variant of Sylvia, associated with meanings related to the forest or woods.
- 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_69ab4c3f2dcc819082df80f5e032f690 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abd90046dc81908bab3440733f1e98 |
completed | March 7, 2026, 7:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af98c2bde8819085fbe1e5221be88d |
completed | March 10, 2026, 4:06 a.m. |
| NEDg | Description generation | batch_69af992fafc88190a95b09b7aebeb5a9 |
completed | March 10, 2026, 4:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69af9983ff8881909273212c6fc6a218 |
completed | March 10, 2026, 4:09 a.m. |
Created at: March 6, 2026, 9:53 p.m.