Triple
T7754804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 27 Dresses |
E175864
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
George
George is a supporting character in the romantic comedy film "27 Dresses," serving as a colleague and love interest within the story’s central wedding-planning world.
|
E685756
|
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: George | Statement: [27 Dresses, character, George]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: George Context triple: [27 Dresses, character, George]
-
A.
George
George is the heroic protagonist of the fantasy film "The Magic Sword," known for embarking on a perilous quest to rescue a princess from an evil sorcerer.
-
B.
George
George is the given name of the Hero of Manila Bay, most famously associated with U.S. Admiral George Dewey, who led the decisive naval victory at the Battle of Manila Bay during the Spanish–American War.
-
C.
George
George is the given name of George Goring, Lord Goring, a prominent Royalist commander during the English Civil War.
-
D.
George
George is the given first name of G. Gordon Liddy, the former FBI agent and key operative in the Watergate scandal.
-
E.
George
George is the given name of George Carnegie, 6th Earl of Northesk, a Scottish nobleman and naval officer in the Royal Navy.
- 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: George Triple: [27 Dresses, character, George]
Generated description
George is a supporting character in the romantic comedy film "27 Dresses," serving as a colleague and love interest within the story’s central wedding-planning world.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: George Target entity description: George is a supporting character in the romantic comedy film "27 Dresses," serving as a colleague and love interest within the story’s central wedding-planning world.
-
A.
George
George is the given name of Lord Goring, a witty and fashionable character in Oscar Wilde’s play "An Ideal Husband."
-
B.
George
George is the given name of American actor George Peppard, best known for starring in the television series "The A-Team" and films such as "Breakfast at Tiffany's."
-
C.
George
George is the heroic protagonist of the fantasy film "The Magic Sword," known for embarking on a perilous quest to rescue a princess from an evil sorcerer.
-
D.
George
George is the naive, vine-swinging jungle hero and main comedic protagonist of the film "George of the Jungle."
-
E.
George
George is a person or character notable primarily for being portrayed as an adversary of Lodac.
- 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_69c6996180088190832e38e8d83ff54a |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c703d851d4819091e9117d3f34cb9a |
completed | March 27, 2026, 10:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8be2b1c5c8190b80029ab6b8b9a3f |
completed | March 29, 2026, 5:52 a.m. |
| NEDg | Description generation | batch_69c8bf8ae464819082afa1b0d9543a91 |
completed | March 29, 2026, 5:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8c0040ad48190be707e706dee6690 |
completed | March 29, 2026, 6 a.m. |
Created at: March 27, 2026, 4:08 p.m.