Triple
T14711230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bella Swan |
E345551
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Isabella
Isabella is the full first name of Bella Swan, the main human protagonist of the Twilight series.
|
E569457
|
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: Isabella | Statement: [Bella Swan, givenName, Isabella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Isabella Context triple: [Bella Swan, givenName, Isabella]
-
A.
Isabella
Isabella is a virtuous and resourceful young noblewoman in Horace Walpole’s Gothic novel "The Castle of Otranto," whose peril and resistance drive much of the story’s suspense and drama.
-
B.
Isabella
Isabella is the given name of Lady Gregory, the influential Irish dramatist, folklorist, and co-founder of Dublin’s Abbey Theatre.
-
C.
Isabella
Isabella was a medieval European queen consort, notably Isabella of France who became Queen of England as the wife of Edward II and played a key role in his overthrow.
-
D.
Isabella
Isabella was a Polish princess of the Jagiellonian dynasty who became Queen consort of Hungary in the 16th century.
-
E.
Isabella
Isabella of Burgundy was a 15th-century duchess consort of Burgundy, known for her political influence and role in the Burgundian court during the late Middle Ages.
- 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: Isabella Triple: [Bella Swan, givenName, Isabella]
Generated description
Isabella is the full first name of Bella Swan, the main human protagonist of the Twilight series.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Isabella Target entity description: Isabella is the full first name of Bella Swan, the main human protagonist of the Twilight series.
-
A.
Isabella
chosen
Isabella is a feminine given name of Spanish and Italian origin, derived from Elizabeth and widely used across many cultures.
-
B.
Isabella
Isabella is a virtuous and resourceful young noblewoman in Horace Walpole’s Gothic novel "The Castle of Otranto," whose peril and resistance drive much of the story’s suspense and drama.
-
C.
Isabella
Isabella is the tragic heroine of John Keats’s narrative poem “Isabella; or, The Pot of Basil,” known for her doomed love and macabre devotion to her murdered lover.
-
D.
Isabella
Isabella is the given name of Mrs Beeton, the famed 19th-century English author of the influential household management guide "Mrs Beeton's Book of Household Management."
-
E.
Isabella
Isabella is the given name of Lady Gregory, the influential Irish dramatist, folklorist, and co-founder of Dublin’s Abbey Theatre.
- F. None of above.
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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb9814e0c8190984ac30d276499cc |
completed | April 14, 2026, 10:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdfb7c7df88190a4e551a12f6e8158 |
completed | May 8, 2026, 3:04 p.m. |
| NEDg | Description generation | batch_69fe08c5131881908d29c8dfc6c0b7cd |
completed | May 8, 2026, 4:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe093c3cf88190bff00387f7516295 |
completed | May 8, 2026, 4:03 p.m. |
Created at: April 10, 2026, 1:28 a.m.