Triple
T7714265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Major League |
E174841
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Lynn Weslin
Lynn Weslin is a fictional character from the baseball comedy film "Major League."
|
E683337
|
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: Lynn Weslin | Statement: [Major League, character, Lynn Weslin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lynn Weslin Context triple: [Major League, character, Lynn Weslin]
-
A.
Janis Allen
Janis Allen is a screenwriter best known for co-writing the 1979 comedy film "Meatballs."
-
B.
Lynn Bracken
Lynn Bracken is a central femme fatale character in the neo-noir crime film "L.A. Confidential," portrayed as a glamorous Veronica Lake look-alike entangled in the movie’s web of corruption and intrigue.
-
C.
Linda Spalding
Linda Spalding is a Canadian-American writer and editor known for her award-winning fiction and non-fiction, including works that explore history, identity, and moral complexity.
-
D.
Lindsey Chapman
Lindsey Chapman is a British television and radio presenter best known for her work on nature and wildlife programmes.
-
E.
Jolene Hunnicutt
Jolene Hunnicutt is a cheerful, down-to-earth waitress from West Virginia who joins the staff at Mel's Diner in the later seasons of the American sitcom "Alice."
- 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: Lynn Weslin Triple: [Major League, character, Lynn Weslin]
Generated description
Lynn Weslin is a fictional character from the baseball comedy film "Major League."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lynn Weslin Target entity description: Lynn Weslin is a fictional character from the baseball comedy film "Major League."
-
A.
Janis Allen
Janis Allen is a screenwriter best known for co-writing the 1979 comedy film "Meatballs."
-
B.
Lynn Bracken
Lynn Bracken is a central femme fatale character in the neo-noir crime film "L.A. Confidential," portrayed as a glamorous Veronica Lake look-alike entangled in the movie’s web of corruption and intrigue.
-
C.
Linda Spalding
Linda Spalding is a Canadian-American writer and editor known for her award-winning fiction and non-fiction, including works that explore history, identity, and moral complexity.
-
D.
Lindsey Chapman
Lindsey Chapman is a British television and radio presenter best known for her work on nature and wildlife programmes.
-
E.
Jolene Hunnicutt
Jolene Hunnicutt is a cheerful, down-to-earth waitress from West Virginia who joins the staff at Mel's Diner in the later seasons of the American sitcom "Alice."
- 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_69c6995c463c8190a14458036249d419 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702ca8f048190a6ea27b8cee2f93e |
completed | March 27, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8acd5e32c8190869834b21aeae8a7 |
completed | March 29, 2026, 4:38 a.m. |
| NEDg | Description generation | batch_69c8ae0383688190be1dbd27262fe717 |
completed | March 29, 2026, 4:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8ae85027c81908a7871ebcb0d27bd |
completed | March 29, 2026, 4:45 a.m. |
Created at: March 27, 2026, 4:04 p.m.