Triple
T11680533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | American Heart |
E277602
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Phil Kellogg
Phil Kellogg is a music producer known for his work with the band American Heart.
|
E969631
|
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: Phil Kellogg | Statement: [American Heart, producer, Phil Kellogg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Phil Kellogg Context triple: [American Heart, producer, Phil Kellogg]
-
A.
Tom Kellogg
Tom Kellogg was an American industrial designer best known for his influential work on innovative automobile designs in the mid-20th century.
-
B.
Jeff Kellogg
Jeff Kellogg is a former Major League Baseball umpire who worked numerous high-profile games, including postseason and World Series matchups.
-
C.
John Dombrowski
John Dombrowski is a notable individual who carries the Dombrowski surname, recognized enough to be specifically cited as a bearer of the name.
-
D.
Ted Cheesman
Ted Cheesman was a film editor best known for his work on classic Hollywood productions, including the 1933 monster film "King Kong."
-
E.
Hugh Kaul
Hugh Kaul was a prominent Birmingham, Alabama businessman and philanthropist known for his significant support of the arts and cultural institutions.
- 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: Phil Kellogg Triple: [American Heart, producer, Phil Kellogg]
Generated description
Phil Kellogg is a music producer known for his work with the band American Heart.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Phil Kellogg Target entity description: Phil Kellogg is a music producer known for his work with the band American Heart.
-
A.
Tom Kellogg
Tom Kellogg was an American industrial designer best known for his influential work on innovative automobile designs in the mid-20th century.
-
B.
Jeff Kellogg
Jeff Kellogg is a former Major League Baseball umpire who worked numerous high-profile games, including postseason and World Series matchups.
-
C.
John Dombrowski
John Dombrowski is a notable individual who carries the Dombrowski surname, recognized enough to be specifically cited as a bearer of the name.
-
D.
Ted Cheesman
Ted Cheesman was a film editor best known for his work on classic Hollywood productions, including the 1933 monster film "King Kong."
-
E.
Hugh Kaul
Hugh Kaul was a prominent Birmingham, Alabama businessman and philanthropist known for his significant support of the arts and cultural institutions.
- 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_69d6aafd0a448190b44da30af8c6c519 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a461b0908190bef4e1c6777affcf |
completed | April 10, 2026, 7:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60a527dc08190a3ce08ddedaa5753 |
completed | May 2, 2026, 2:29 p.m. |
| NEDg | Description generation | batch_69f60cca20f48190b8e6e591144f252e |
completed | May 2, 2026, 2:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f60d4c30448190874f253b864ef61e |
completed | May 2, 2026, 2:42 p.m. |
Created at: April 8, 2026, 9:40 p.m.