Triple
T14044148
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Von Bondies |
E337914
|
entity |
| Predicate | formerMember |
P1168
|
FINISHED |
| Object | Don Blum |
E1155977
|
NE FINISHED |
How this triple was built (2 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: Don Blum | Statement: [The Von Bondies, formerMember, Don Blum]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Don Blum Context triple: [The Von Bondies, formerMember, Don Blum]
-
A.
Don Blum
chosen
Don Blum is an American rock drummer best known for his work with the Detroit garage rock band The Von Bondies.
-
B.
Len Blum
Len Blum is a Canadian screenwriter known for his work on numerous comedy films, including the 2006 reboot of The Pink Panther.
-
C.
Norman Bluhm
Norman Bluhm was an American abstract expressionist painter known for his dynamic, gestural canvases that bridged action painting and lyrical abstraction.
-
D.
Dennis Dreith
Dennis Dreith is an American composer, orchestrator, and music industry executive known for his work on film and television scores and for advocating for musicians’ rights.
-
E.
Michael Blum
Michael Blum is best known as the husband of comedian and actress Julia Sweeney.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d81c664e48819088cbd8f433aeffe5 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de312b94308190bd0961f5bc719c7b |
completed | April 14, 2026, 12:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d379bc881908b7954c633787165 |
completed | May 9, 2026, 1:57 p.m. |
Created at: April 9, 2026, 10:20 p.m.