Triple
T3287489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bella Swan |
E69017
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Bella
Bella is the main human protagonist of the Twilight series, known for her introspective nature and complex relationship with the supernatural world.
|
E345551
|
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: Bella | Statement: [Bella Swan, nickname, Bella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bella Context triple: [Bella Swan, nickname, Bella]
-
A.
Bella Greene
Bella Greene is a relatively obscure individual whose specific public achievements or background are not widely documented.
-
B.
Esme
Esme is a song by Joanna Newsom from her 2010 album "Have One on Me," noted for its intricate harp arrangements and poetic lyrics.
-
C.
Tessa
Tessa is a feminine given name commonly used in English-speaking countries, often as a diminutive of Theresa or Therese.
-
D.
Zoe
Zoe is a feminine given name of Greek origin meaning "life," commonly used in many English-speaking and European countries.
-
E.
Bella Higginbotham
Bella Higginbotham is an American actress best known for her role in the film "Troop Zero" and for appearing in various television and streaming series.
- 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: Bella Triple: [Bella Swan, nickname, Bella]
Generated description
Bella is the main human protagonist of the Twilight series, known for her introspective nature and complex relationship with the supernatural world.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bella Target entity description: Bella is the main human protagonist of the Twilight series, known for her introspective nature and complex relationship with the supernatural world.
-
A.
Bella Greene
Bella Greene is a relatively obscure individual whose specific public achievements or background are not widely documented.
-
B.
Esme
Esme is a song by Joanna Newsom from her 2010 album "Have One on Me," noted for its intricate harp arrangements and poetic lyrics.
-
C.
Tessa
Tessa is a feminine given name commonly used in English-speaking countries, often as a diminutive of Theresa or Therese.
-
D.
Zoe
Zoe is a feminine given name of Greek origin meaning "life," commonly used in many English-speaking and European countries.
-
E.
Bella Higginbotham
Bella Higginbotham is an American actress best known for her role in the film "Troop Zero" and for appearing in various television and streaming series.
- 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_69ad859d45748190b0742408c954b39f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb058e00881908fdf0a23208860d4 |
completed | March 8, 2026, 5:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2e85f71508190b194b4d383d7ee32 |
completed | March 12, 2026, 4:22 p.m. |
| NEDg | Description generation | batch_69b2e8d165488190bdb6c07257f7502a |
completed | March 12, 2026, 4:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2ecfd3c20819089bc0b2141aee8eb |
completed | March 12, 2026, 4:42 p.m. |
Created at: March 8, 2026, 3:10 p.m.