Triple
T11385886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lola Bunny |
E269713
|
entity |
| Predicate | portrayedByVoice |
P13156
|
FINISHED |
| Object |
Candice Brown
Candice Brown is a voice actress known for providing the voice of the animated character Lola Bunny.
|
E922944
|
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: Candice Brown | Statement: [Lola Bunny, portrayedByVoice, Candice Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Candice Brown Context triple: [Lola Bunny, portrayedByVoice, Candice Brown]
-
A.
Candice Crawford
Candice Crawford is an American former beauty queen and television sports reporter who is married to former NFL quarterback Tony Romo.
-
B.
Candice Patton
Candice Patton is an American actress best known for her role as Iris West-Allen in the superhero television series "The Flash."
-
C.
Candice Neil
Candice Neil is an American model best known for her long-term relationship with actor Billy Zane, with whom she has two children.
-
D.
Candice Marie Pratt
Candice Marie Pratt is the earnest, nature-loving protagonist of Mike Leigh’s 1976 British television film "Nuts in May."
-
E.
Meghan Payton
Meghan Payton is the daughter of longtime NFL head coach Sean Payton and has worked as a sports media personality and reporter.
- 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: Candice Brown Triple: [Lola Bunny, portrayedByVoice, Candice Brown]
Generated description
Candice Brown is a voice actress known for providing the voice of the animated character Lola Bunny.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Candice Brown Target entity description: Candice Brown is a voice actress known for providing the voice of the animated character Lola Bunny.
-
A.
Candice Crawford
Candice Crawford is an American former beauty queen and television sports reporter who is married to former NFL quarterback Tony Romo.
-
B.
Candice Patton
Candice Patton is an American actress best known for her role as Iris West-Allen in the superhero television series "The Flash."
-
C.
Candice Neil
Candice Neil is an American model best known for her long-term relationship with actor Billy Zane, with whom she has two children.
-
D.
Candice Marie Pratt
Candice Marie Pratt is the earnest, nature-loving protagonist of Mike Leigh’s 1976 British television film "Nuts in May."
-
E.
Meghan Payton
Meghan Payton is the daughter of longtime NFL head coach Sean Payton and has worked as a sports media personality and reporter.
- 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_69d6aacdbc6c8190af6dc3d5f5d22836 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7fc378d808190b587a044ede67e1e |
completed | April 9, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e58c3c9a7081908002d726ec9e7715 |
completed | April 20, 2026, 2:15 a.m. |
| NEDg | Description generation | batch_69e5932d3cb88190807acdcdc3aaa9fc |
completed | April 20, 2026, 2:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e59a0ab7e081908cb8761c4f82c664 |
completed | April 20, 2026, 3:14 a.m. |
Created at: April 8, 2026, 9:34 p.m.