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.