Triple
T17450013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jimmy Garoppolo |
E424889
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Jimmy G
Jimmy G is an American football quarterback best known for his time with the San Francisco 49ers, including leading them to a Super Bowl appearance.
|
E1269351
|
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: Jimmy G | Statement: [Jimmy Garoppolo, nickname, Jimmy G]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jimmy G Context triple: [Jimmy Garoppolo, nickname, Jimmy G]
-
A.
Jimmy Ba
Jimmy Ba is a prominent machine learning researcher known for his work on deep learning optimization methods such as the Adam optimizer.
-
B.
Jimmy
Jimmy is the given name of American actor Jimmy Smits, known for his roles in television series such as "L.A. Law," "NYPD Blue," and "The West Wing."
-
C.
Jimmy
Jimmy is a timid, imaginative, and often melodramatic young boy from the animated series "Ed, Edd n Eddy," known for his close friendship with Sarah and frequent misadventures.
-
D.
Jimmy
"Jimmy" is a notable song by the Chicago-based hip-hop collective Pivot Gang.
-
E.
Jimmy
Jimmy is a character in the Halloween horror film franchise, notably appearing as a sympathetic hospital orderly in Halloween II.
- 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: Jimmy G Triple: [Jimmy Garoppolo, nickname, Jimmy G]
Generated description
Jimmy G is an American football quarterback best known for his time with the San Francisco 49ers, including leading them to a Super Bowl appearance.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jimmy G Target entity description: Jimmy G is an American football quarterback best known for his time with the San Francisco 49ers, including leading them to a Super Bowl appearance.
-
A.
Jimmy Ba
Jimmy Ba is a prominent machine learning researcher known for his work on deep learning optimization methods such as the Adam optimizer.
-
B.
Jimmy
Jimmy is the given name of American actor Jimmy Smits, known for his roles in television series such as "L.A. Law," "NYPD Blue," and "The West Wing."
-
C.
Jimmy
Jimmy is a timid, imaginative, and often melodramatic young boy from the animated series "Ed, Edd n Eddy," known for his close friendship with Sarah and frequent misadventures.
-
D.
Jimmy
"Jimmy" is a notable song by the Chicago-based hip-hop collective Pivot Gang.
-
E.
Jimmy
Jimmy is a character in the Halloween horror film franchise, notably appearing as a sympathetic hospital orderly in Halloween II.
- 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_69d889db0ba481908402409af3b37917 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4513d00f48190802a3bdc8c8f4db5 |
completed | April 19, 2026, 3:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01affcfcc88190b3dcb6b267946919 |
completed | May 11, 2026, 10:31 a.m. |
| NEDg | Description generation | batch_6a01b164864481909673d9351bb7f817 |
completed | May 11, 2026, 10:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01b251040c8190a1d030cb55e4f052 |
completed | May 11, 2026, 10:41 a.m. |
Created at: April 10, 2026, 5:47 a.m.