Triple
T22577937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Quest |
E544465
|
entity |
| Predicate | collaboratedWith |
P435
|
FINISHED |
| Object | Dom Kennedy |
—
|
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: Dom Kennedy | Statement: [Lord Quest, collaboratedWith, Dom Kennedy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dom Kennedy Context triple: [Lord Quest, collaboratedWith, Dom Kennedy]
-
A.
Dom Kennedy
chosen
Dom Kennedy is a Los Angeles-based hip-hop artist known for his laid-back West Coast sound and independent releases that have cultivated a strong underground following.
-
B.
Ryan Kennedy
Ryan Kennedy is a Canadian actor known for his roles in television series such as the crime drama "Tin Star."
-
C.
Chris Kennedy
Chris Kennedy is an Australian filmmaker best known for his work in independent cinema, including directing the feature film "Doing Time for Patsy Cline."
-
D.
Sam Kennedy
Sam Kennedy is a Major League Baseball executive best known for serving as the president and CEO of the Boston Red Sox.
-
E.
Sam Kennedy
Sam Kennedy is the central detective protagonist in the 2002 crime thriller film "Murder by Numbers," investigating a meticulously planned murder committed by two high school students.
- 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_69e11e30d05481909df915354c89f0d6 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f15feee27c8190b31c923e1f00a363 |
completed | April 29, 2026, 1:33 a.m. |
Created at: April 16, 2026, 8:53 p.m.