Triple
T19327598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Destroy All Monsters |
E483399
|
entity |
| Predicate | founder |
P104
|
FINISHED |
| Object | Jim Shaw |
—
|
NE NERFINISHED |
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: Jim Shaw | Statement: [Destroy All Monsters, founder, Jim Shaw]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jim Shaw Context triple: [Destroy All Monsters, founder, Jim Shaw]
-
A.
Jim Shaw
chosen
Jim Shaw is an American artist and musician best known for his influential role in the experimental rock band Destroy All Monsters and his conceptually driven visual art.
-
B.
Frank Shaw
Frank Shaw is an individual known primarily as the child of Mrs. Shaw, though further widely recognized biographical details are not specified.
-
C.
Stan Shaw
Stan Shaw is an American character actor known for his roles in films such as "Harlem Nights," "Rocky," and "The Boys in Company C."
-
D.
Chris Shaw
Chris Shaw is a musician best known as a member of the garage rock band GØGGS.
-
E.
Chris Shaw
Chris Shaw is a British journalist and television executive, known for his work in UK broadcasting and as the husband of broadcaster Martha Kearney.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d13e3c81909d91d1d5ec37c095 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e6163f32f48190be17cccf4e537372 |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 10, 2026, 1:33 p.m.