Triple
T21428889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Cartellone |
E528630
|
entity |
| Predicate | memberOf |
P10
|
FINISHED |
| Object | Accept |
—
|
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: Accept | Statement: [Michael Cartellone, memberOf, Accept]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Accept Context triple: [Michael Cartellone, memberOf, Accept]
-
A.
Accept
chosen
Accept is a German heavy metal band, formed in the 1970s, known as one of the pioneering groups of the speed and thrash metal-influencing Teutonic metal scene.
-
B.
Acceptance
Acceptance is a science fiction novel by Jeff VanderMeer that concludes the Southern Reach Trilogy, a key work in the New Weird genre blending ecological horror, mystery, and surrealism.
-
C.
Acceptance
Acceptance is an American alternative rock band known for its melodic sound and emotionally driven lyrics, emerging from the early 2000s rock scene.
-
D.
Accepted
Accepted is a 2006 American comedy film about a high school slacker who creates a fake college after being rejected from every university he applied to.
-
E.
AcceptanceNOW
AcceptanceNOW is a lease-to-own and rent-to-own service brand that provides flexible payment options for furniture, electronics, and appliances, typically through in-store kiosks at retail partners.
- 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_69e0c455f3688190810bc96365791b0f |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee813db52c8190ac933bc6ec4dbf77 |
completed | April 26, 2026, 9:18 p.m. |
Created at: April 16, 2026, 5:49 p.m.