Triple
T14955946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bernard "Beanie" Campbell |
E372925
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Beanie |
E1129212
|
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: Beanie | Statement: [Bernard "Beanie" Campbell, nickname, Beanie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beanie Context triple: [Bernard "Beanie" Campbell, nickname, Beanie]
-
A.
Beanie
chosen
Beanie is a character from the comedy film "Old School," known as one of the middle-aged men who start an off-campus fraternity.
-
B.
Beany
Beany is a cartoon character best known as the young, beanie-capped hero of Bob Clampett’s puppet and animated series "Time for Beany" and "Beany and Cecil."
-
C.
The Beanie Bubble
The Beanie Bubble is a 2023 comedy-drama film co-written and co-directed by Kristin Gore that explores the rise and fall of the Beanie Babies craze of the 1990s.
-
D.
Bunny
Bunny is a supporting character in the psychological thriller film "Don't Worry Darling," portrayed as a seemingly content housewife whose role becomes more complex as the story’s unsettling reality is revealed.
-
E.
Bunny
Bunny is an Oscar-winning animated short film by Blue Sky Studios, renowned for its emotional storytelling and pioneering use of computer-generated imagery.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6cc73848190ac181782b20dc838 |
completed | April 15, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe8bda691481909c2d89a362782ed8 |
completed | May 9, 2026, 1:20 a.m. |
Created at: April 10, 2026, 2:40 a.m.