Triple
T21527560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Calibre |
E531134
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Vaughn
Vaughn is the protagonist of the science fiction novel "Calibre," around whom the story’s central events and conflicts revolve.
|
E1489690
|
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: Vaughn | Statement: [Calibre, mainCharacter, Vaughn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vaughn Context triple: [Calibre, mainCharacter, Vaughn]
-
A.
Vaughn
Vaughn is a surname most prominently associated with English film director and producer Matthew Vaughn, known for stylish action and comic-book adaptations.
-
B.
LaVaughn
LaVaughn is the middle name of Michelle Obama, the former First Lady of the United States.
-
C.
Gaven
Gaven is a suburb on the Gold Coast in Queensland, Australia, known for its semi-rural character and proximity to major transport routes.
-
D.
Vaughn Towers
Vaughn Towers is a multi-level premium seating and press facility at NC State University's Carter–Finley Stadium, housing luxury suites, club seats, and media operations.
-
E.
Corbin
Corbin is a surname most notably associated with American professional baseball pitcher Patrick Corbin.
- 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: Vaughn Triple: [Calibre, mainCharacter, Vaughn]
Generated description
Vaughn is the protagonist of the science fiction novel "Calibre," around whom the story’s central events and conflicts revolve.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vaughn Target entity description: Vaughn is the protagonist of the science fiction novel "Calibre," around whom the story’s central events and conflicts revolve.
-
A.
Vaughn
Vaughn is a surname most prominently associated with English film director and producer Matthew Vaughn, known for stylish action and comic-book adaptations.
-
B.
LaVaughn
LaVaughn is the middle name of Michelle Obama, the former First Lady of the United States.
-
C.
Gaven
Gaven is a suburb on the Gold Coast in Queensland, Australia, known for its semi-rural character and proximity to major transport routes.
-
D.
Vaughn Towers
Vaughn Towers is a multi-level premium seating and press facility at NC State University's Carter–Finley Stadium, housing luxury suites, club seats, and media operations.
-
E.
Corbin
Corbin is a surname most notably associated with American professional baseball pitcher Patrick Corbin.
- 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_69e0c45e5b8881908ac18fc2f493b114 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee88522e948190b9fa5a3587f32eae |
completed | April 26, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09e82b294481908112b57d4d00103d |
completed | May 17, 2026, 4:09 p.m. |
| NEDg | Description generation | batch_6a09e8c687fc81908d3b8b7a2cdfedff |
completed | May 17, 2026, 4:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09e93ff78c819099a9fed7a4874ba5 |
completed | May 17, 2026, 4:13 p.m. |
Created at: April 16, 2026, 6:26 p.m.