Triple
T22543258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Let Love |
E557350
|
entity |
| Predicate | hasArtist |
P5936
|
FINISHED |
| Object | Boom Bishop |
—
|
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: Boom Bishop | Statement: [Let Love, hasArtist, Boom Bishop]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Boom Bishop Context triple: [Let Love, hasArtist, Boom Bishop]
-
A.
Boom Bishop
chosen
Boom Bishop is a music producer best known for his work on the project "Let Love."
-
B.
Bibbo Bibbowski
Bibbo Bibbowski is a good-hearted Metropolis dockworker and bar owner in DC Comics who serves as one of Superman’s most loyal human friends and supporters.
-
C.
Boomhauer
Boomhauer is a fast-talking, mumbling ladies’ man and laid-back neighbor known for his distinctive speech pattern on the animated TV series "King of the Hill."
-
D.
Patrick Smash
Patrick Smash is the flatulent young protagonist of the British family comedy film "Thunderpants," whose extraordinary gas powers lead him on an unlikely adventure.
-
E.
Bootleg Pete
Bootleg Pete is a classic Disney villain character, typically portrayed as a burly, antagonistic foil to Mickey Mouse and his friends.
- 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_69e11e58662081909ae346ab384514ca |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15f33c9cc819086d098f36e4121dc |
completed | April 29, 2026, 1:30 a.m. |
Created at: April 16, 2026, 8:51 p.m.