Triple
T15982151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Affton, Missouri |
E387601
|
entity |
| Predicate | hasNotableResident |
P1092
|
FINISHED |
| Object |
Jim Talent
Jim Talent is an American Republican politician and former U.S. Senator from Missouri.
|
E1188663
|
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: Jim Talent | Statement: [Affton, Missouri, hasNotableResident, Jim Talent]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jim Talent Context triple: [Affton, Missouri, hasNotableResident, Jim Talent]
-
A.
Tinsley Ellis
Tinsley Ellis is an American blues and rock guitarist, singer, and songwriter known for his fiery guitar work and extensive touring career.
-
B.
Dick La Reno
Dick La Reno was an early 20th-century American silent film actor known for his roles in Westerns and historical dramas.
-
C.
Jeff Morton
Jeff Morton is a television producer best known for his work as an executive producer on the comedy series "Life in Pieces."
-
D.
Guymon Casady
Guymon Casady is an American film and television producer and talent manager, known for his work on projects such as "Office Christmas Party" and the series "Game of Thrones."
-
E.
Shepherd Henderson
Shepherd Henderson is the romantic lead in the 1958 fantasy–romantic comedy film "Bell, Book and Candle," where he becomes the unwitting target of a modern witch’s love spell.
- 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: Jim Talent Triple: [Affton, Missouri, hasNotableResident, Jim Talent]
Generated description
Jim Talent is an American Republican politician and former U.S. Senator from Missouri.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jim Talent Target entity description: Jim Talent is an American Republican politician and former U.S. Senator from Missouri.
-
A.
Tinsley Ellis
Tinsley Ellis is an American blues and rock guitarist, singer, and songwriter known for his fiery guitar work and extensive touring career.
-
B.
Dick La Reno
Dick La Reno was an early 20th-century American silent film actor known for his roles in Westerns and historical dramas.
-
C.
Jeff Morton
Jeff Morton is a television producer best known for his work as an executive producer on the comedy series "Life in Pieces."
-
D.
Guymon Casady
Guymon Casady is an American film and television producer and talent manager, known for his work on projects such as "Office Christmas Party" and the series "Game of Thrones."
-
E.
Shepherd Henderson
Shepherd Henderson is the romantic lead in the 1958 fantasy–romantic comedy film "Bell, Book and Candle," where he becomes the unwitting target of a modern witch’s love spell.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e15755b5548190acfa29eecb11e675 |
completed | April 16, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3cb0ed48190b35c19f3961f183b |
completed | May 9, 2026, 11:31 p.m. |
| NEDg | Description generation | batch_69ffc5f664148190a1f400c28d31cafe |
completed | May 9, 2026, 11:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffc6f9b4f4819092600165241377f6 |
completed | May 9, 2026, 11:44 p.m. |
Created at: April 10, 2026, 4:54 a.m.