Triple
T13970092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brewers |
E336035
|
entity |
| Predicate | athleticDirector |
P745
|
FINISHED |
| Object |
Michelle Walsh
Michelle Walsh is a sports administrator who serves as the athletic director for the Brewers organization.
|
E1090032
|
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: Michelle Walsh | Statement: [Brewers, athleticDirector, Michelle Walsh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michelle Walsh Context triple: [Brewers, athleticDirector, Michelle Walsh]
-
A.
Jennifer Walsh
Jennifer Walsh is a personal name shared by multiple individuals, including professionals in fields such as academia, sports, and the arts.
-
B.
Megan Walsh
Megan Walsh is the teenage government-trained assassin who goes undercover as a high school student in the action-comedy film "Barely Lethal."
-
C.
Lisa O'Brien
Lisa O'Brien is the mother of American actor Dylan O'Brien, known for his roles in "Teen Wolf" and "The Maze Runner" film series.
-
D.
Kay Walsh
Kay Walsh was a British actress and dancer known for her versatile performances in mid-20th-century cinema and her collaborations with prominent directors like David Lean.
-
E.
Jennifer Tighe
Jennifer Tighe is an American actress known for her work in television, film, and theater, and as the daughter of actor Kevin Tighe.
- 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: Michelle Walsh Triple: [Brewers, athleticDirector, Michelle Walsh]
Generated description
Michelle Walsh is a sports administrator who serves as the athletic director for the Brewers organization.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michelle Walsh Target entity description: Michelle Walsh is a sports administrator who serves as the athletic director for the Brewers organization.
-
A.
Jennifer Walsh
Jennifer Walsh is a personal name shared by multiple individuals, including professionals in fields such as academia, sports, and the arts.
-
B.
Megan Walsh
Megan Walsh is the teenage government-trained assassin who goes undercover as a high school student in the action-comedy film "Barely Lethal."
-
C.
Lisa O'Brien
Lisa O'Brien is the mother of American actor Dylan O'Brien, known for his roles in "Teen Wolf" and "The Maze Runner" film series.
-
D.
Kay Walsh
Kay Walsh was a British actress and dancer known for her versatile performances in mid-20th-century cinema and her collaborations with prominent directors like David Lean.
-
E.
Jennifer Tighe
Jennifer Tighe is an American actress known for her work in television, film, and theater, and as the daughter of actor Kevin Tighe.
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e8daeac8190aadd4b3b60222482 |
completed | April 14, 2026, 12:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd323e89948190bb280e93e2058c0a |
completed | May 8, 2026, 12:45 a.m. |
| NEDg | Description generation | batch_69fd33d981408190b017164a04676f4c |
completed | May 8, 2026, 12:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd3437f90081908c325f0a36993f57 |
completed | May 8, 2026, 12:54 a.m. |
Created at: April 9, 2026, 10:18 p.m.