Triple
T14177638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Cooler |
E351373
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object |
Frank Hannah
Frank Hannah is a Scottish-born screenwriter best known for co-writing the crime drama film "The Cooler."
|
E1084570
|
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: Frank Hannah | Statement: [The Cooler, writer, Frank Hannah]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frank Hannah Context triple: [The Cooler, writer, Frank Hannah]
-
A.
Mel Sharples
Mel Sharples is a gruff but good-hearted diner owner and cook from the sitcom "Alice," known for his no-nonsense attitude and catchphrase, "Stow it!"
-
B.
George Hildebrand
George Hildebrand was an American Major League Baseball umpire active in the early 20th century.
-
C.
Merritt Andrews
Merritt Andrews is a fictional character played by actress Yvette Mimieux, best known from her work in mid-20th-century American film and television.
-
D.
Pat Hanrahan
Pat Hanrahan is a computer graphics pioneer, Stanford professor, and Turing Award–winning researcher best known for co-founding Tableau and his influential work on rendering and visualization.
-
E.
Aaron Hilmer
Aaron Hilmer is a German actor best known internationally for his role in the 2022 adaptation of "All Quiet on the Western Front."
- 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: Frank Hannah Triple: [The Cooler, writer, Frank Hannah]
Generated description
Frank Hannah is a Scottish-born screenwriter best known for co-writing the crime drama film "The Cooler."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Frank Hannah Target entity description: Frank Hannah is a Scottish-born screenwriter best known for co-writing the crime drama film "The Cooler."
-
A.
Mel Sharples
Mel Sharples is a gruff but good-hearted diner owner and cook from the sitcom "Alice," known for his no-nonsense attitude and catchphrase, "Stow it!"
-
B.
George Hildebrand
George Hildebrand was an American Major League Baseball umpire active in the early 20th century.
-
C.
Merritt Andrews
Merritt Andrews is a fictional character played by actress Yvette Mimieux, best known from her work in mid-20th-century American film and television.
-
D.
Pat Hanrahan
Pat Hanrahan is a computer graphics pioneer, Stanford professor, and Turing Award–winning researcher best known for co-founding Tableau and his influential work on rendering and visualization.
-
E.
Aaron Hilmer
Aaron Hilmer is a German actor best known internationally for his role in the 2022 adaptation of "All Quiet on the Western Front."
- 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_69d8278834a08190b0f1784e58d7b99c |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61c76e8081909994b95b631100e9 |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcf80f03a48190a5374fb6374255a8 |
completed | May 7, 2026, 8:37 p.m. |
| NEDg | Description generation | batch_69fd09b00dc48190bec9853e3dc78f26 |
completed | May 7, 2026, 9:52 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd0a4c377c81909af76e8bd47e2f2a |
completed | May 7, 2026, 9:55 p.m. |
Created at: April 10, 2026, 1:02 a.m.