Triple
T10553975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | One on One |
E249025
|
entity |
| Predicate | spinOff |
P7736
|
FINISHED |
| Object | Cuts |
E131537
|
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: Cuts | Statement: [One on One, spinOff, Cuts]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cuts Context triple: [One on One, spinOff, Cuts]
-
A.
Cuts
Cuts is an American television sitcom that aired on UPN, featuring Shannon Elizabeth in a comedic role set around a family-owned barbershop.
-
B.
CUT
CUT is the National Rail station code assigned to Cutty Sark DLR station in London.
-
C.
CUT
CUT is the commonly used acronym for the Central University of Technology, a higher education institution in South Africa.
-
D.
CUT
CUT is a public university in Limassol, Cyprus, known for its focus on applied research and technology-oriented academic programs.
-
E.
Cuts (TV series)
chosen
Cuts is an American sitcom that aired on UPN in the mid-2000s, focusing on the comedic ups and downs of running a family-owned barbershop in Baltimore.
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d527118da081909ca61bc555a17609 |
completed | April 7, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9346f6a38819087647e7a09f40c41 |
completed | April 10, 2026, 5:33 p.m. |
Created at: April 6, 2026, 12:34 p.m.