Triple
T3972845
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pump |
E92371
|
entity |
| Predicate | mainPerformer |
P1363
|
FINISHED |
| Object | Joey Kramer |
E308732
|
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: Joey Kramer | Statement: [Pump, mainPerformer, Joey Kramer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joey Kramer Context triple: [Pump, mainPerformer, Joey Kramer]
-
A.
Joey Kramer
chosen
Joey Kramer is an American drummer best known as the longtime percussionist for the rock band Aerosmith.
-
B.
Deryck Guyler
Deryck Guyler was a British character actor and comedian best known for his roles in mid-20th-century radio and television sitcoms.
-
C.
Mark Morton
Mark Morton was a prominent American industrialist and executive associated with the Morton Salt company and a notable member of the Morton family.
-
D.
Michael Bergen
Michael Bergen is the charming yet immature college student and central character played by Ryan Reynolds in the sitcom "Two Guys and a Girl."
-
E.
Joe Darlington
Joe Darlington is a notable individual who shares the Darlington surname, recognized enough to be specifically cited as a bearer of the name.
- 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_69aed96624188190ac8c45bb57ab72b5 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef996ff6c8190ba8cc490e4744b95 |
completed | March 9, 2026, 4:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b54c443e208190a8bf83ec642a142e |
completed | March 14, 2026, 11:53 a.m. |
Created at: March 9, 2026, 3:32 p.m.