Triple
T3798613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 50 First Dates |
E91633
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
George Wing
George Wing is an American screenwriter best known for writing the romantic comedy film "50 First Dates."
|
E389180
|
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 Wing | Statement: [50 First Dates, screenwriter, George Wing]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: George Wing Context triple: [50 First Dates, screenwriter, George Wing]
-
A.
David Wingate
David Wingate is a former American professional basketball player who played in the NBA primarily as a defensive-minded guard and swingman during the late 1980s and 1990s.
-
B.
George Wall
George Wall is a relatively obscure individual whose specific notability is not clearly identifiable from the given information alone.
-
C.
David Wingo
David Wingo is an American film and television composer known for his atmospheric, character-driven scores for independent dramas and series such as "Take Shelter," "Mud," and HBO's "Barry."
-
D.
R. Douglas Wright
R. Douglas Wright is a distinguished American trombonist and educator known for his prominent orchestral and conservatory teaching roles.
-
E.
Tom Wright
Tom Wright is a British architect best known for designing Dubai’s iconic sail-shaped Burj Al Arab hotel.
- 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 Wing Triple: [50 First Dates, screenwriter, George Wing]
Generated description
George Wing is an American screenwriter best known for writing the romantic comedy film "50 First Dates."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: George Wing Target entity description: George Wing is an American screenwriter best known for writing the romantic comedy film "50 First Dates."
-
A.
David Wingate
David Wingate is a former American professional basketball player who played in the NBA primarily as a defensive-minded guard and swingman during the late 1980s and 1990s.
-
B.
George Wall
George Wall is a relatively obscure individual whose specific notability is not clearly identifiable from the given information alone.
-
C.
David Wingo
David Wingo is an American film and television composer known for his atmospheric, character-driven scores for independent dramas and series such as "Take Shelter," "Mud," and HBO's "Barry."
-
D.
R. Douglas Wright
R. Douglas Wright is a distinguished American trombonist and educator known for his prominent orchestral and conservatory teaching roles.
-
E.
Tom Wright
Tom Wright is a British architect best known for designing Dubai’s iconic sail-shaped Burj Al Arab hotel.
- 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_69aed96354f48190a768966d6bd19b04 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee7a270908190ac30537a22f3d500 |
completed | March 9, 2026, 3:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4f061a9e481908d16ae0aa44e2f16 |
completed | March 14, 2026, 5:21 a.m. |
| NEDg | Description generation | batch_69b4f25fab648190abbded4d44357c54 |
completed | March 14, 2026, 5:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4f314a63c8190a76ed8f4bd21eaf5 |
completed | March 14, 2026, 5:33 a.m. |
Created at: March 9, 2026, 3:15 p.m.