Triple
T10810752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tea for Two |
E255092
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Harry Clork
Harry Clork was an American screenwriter active during Hollywood’s classic era, known for contributing to numerous studio comedies and musicals.
|
E887366
|
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: Harry Clork | Statement: [Tea for Two, screenwriter, Harry Clork]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harry Clork Context triple: [Tea for Two, screenwriter, Harry Clork]
-
A.
Harry Compton
Harry Compton is a fictional character from the 1940 American film "Boom Town," which centers on the lives and rivalries of wildcat oil drillers.
-
B.
Gerald Lathbury
Gerald Lathbury was a British Army lieutenant general and distinguished airborne commander during the Second World War.
-
C.
Harry Peacock
Harry Peacock is a British actor known for his work in television comedies and dramas, and as the son of actor and songwriter Trevor Peacock.
-
D.
Harry Lonsdale
Harry Lonsdale was an early 20th-century actor known for his roles in silent films.
-
E.
Harry Bertram
Harry Bertram is the long-lost heir whose disappearance and eventual restoration to his family’s estate drive the central plot of Sir Walter Scott’s novel "Guy Mannering."
- 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: Harry Clork Triple: [Tea for Two, screenwriter, Harry Clork]
Generated description
Harry Clork was an American screenwriter active during Hollywood’s classic era, known for contributing to numerous studio comedies and musicals.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Harry Clork Target entity description: Harry Clork was an American screenwriter active during Hollywood’s classic era, known for contributing to numerous studio comedies and musicals.
-
A.
Harry Compton
Harry Compton is a fictional character from the 1940 American film "Boom Town," which centers on the lives and rivalries of wildcat oil drillers.
-
B.
Gerald Lathbury
Gerald Lathbury was a British Army lieutenant general and distinguished airborne commander during the Second World War.
-
C.
Harry Peacock
Harry Peacock is a British actor known for his work in television comedies and dramas, and as the son of actor and songwriter Trevor Peacock.
-
D.
Harry Lonsdale
Harry Lonsdale was an early 20th-century actor known for his roles in silent films.
-
E.
Harry Bertram
Harry Bertram is the long-lost heir whose disappearance and eventual restoration to his family’s estate drive the central plot of Sir Walter Scott’s novel "Guy Mannering."
- 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d733b7bfac8190b6ae34144376d6ad |
completed | April 9, 2026, 5:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de8526237881908dc3b25b16de7871 |
completed | April 14, 2026, 6:19 p.m. |
| NEDg | Description generation | batch_69de8954500c81909b57c4f8007959aa |
completed | April 14, 2026, 6:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69de8f38e3048190b1acc81bb56fe165 |
completed | April 14, 2026, 7:02 p.m. |
Created at: April 8, 2026, 9:18 p.m.