Triple
T9653046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sofia the First |
E233381
|
entity |
| Predicate | featuresCrossoverCharacter |
P8704
|
FINISHED |
| Object | Belle |
E285208
|
NE FINISHED |
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: Belle | Statement: [Sofia the First, featuresCrossoverCharacter, Belle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belle Context triple: [Sofia the First, featuresCrossoverCharacter, Belle]
-
A.
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.
-
B.
Belle
Belle is the official mascot character representing Bennett College and its community spirit.
-
C.
Belle
chosen
Belle is the intelligent, book-loving heroine of Disney’s "Beauty and the Beast," known for her compassion, independence, and iconic yellow ball gown.
-
D.
Belle
Belle Roosevelt was an American socialite and member of the prominent Roosevelt family in the late 19th and early 20th centuries.
-
E.
Belle Bennett
Belle Bennett was an American stage and silent film actress best known for her emotionally powerful performances in early 20th-century cinema.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca848c1ba88190b84b410cd14627fc |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9bb26b748190bc32e2003829b0ec |
completed | April 1, 2026, 10:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d18270dee481909c8d2de1fafdaf5b |
completed | April 4, 2026, 9:28 p.m. |
Created at: March 30, 2026, 8:13 p.m.