Triple
T16429903
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Owsla |
E399045
|
entity |
| Predicate | notableArtist |
P601
|
FINISHED |
| Object |
Kill The Noise
Kill The Noise is an American electronic music producer and DJ known for his heavy bass-driven sound and collaborations across dubstep, electro house, and drum and bass.
|
E1213394
|
NE FINISHED |
How this triple was built (4 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: Kill The Noise | Statement: [Owsla, notableArtist, Kill The Noise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kill The Noise Context triple: [Owsla, notableArtist, Kill The Noise]
-
A.
Bring the Noise
"Bring the Noise" is a pioneering 1987 hip hop track by Public Enemy known for its politically charged lyrics, dense production, and major influence on both rap and rap-rock.
-
B.
Kill You
"Kill You" is a controversial and aggressive rap song by Eminem from his acclaimed album "The Marshall Mathers LP."
-
C.
Makin' Some Noise
"Makin' Some Noise" is a rock song by Tom Petty and the Heartbreakers featured on their early-1990s album.
-
D.
Killing Machine
Killing Machine is a film featuring American model and actress Margaux Hemingway in a prominent role.
-
E.
It Won't Kill Ya
"It Won't Kill Ya" is a dance-pop track by The Chainsmokers featuring Louane, known for its upbeat production and themes of taking risks in love.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kill The Noise Triple: [Owsla, notableArtist, Kill The Noise]
Generated description
Kill The Noise is an American electronic music producer and DJ known for his heavy bass-driven sound and collaborations across dubstep, electro house, and drum and bass.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kill The Noise Target entity description: Kill The Noise is an American electronic music producer and DJ known for his heavy bass-driven sound and collaborations across dubstep, electro house, and drum and bass.
-
A.
Bring the Noise
"Bring the Noise" is a pioneering 1987 hip hop track by Public Enemy known for its politically charged lyrics, dense production, and major influence on both rap and rap-rock.
-
B.
Kill You
"Kill You" is a controversial and aggressive rap song by Eminem from his acclaimed album "The Marshall Mathers LP."
-
C.
Makin' Some Noise
"Makin' Some Noise" is a rock song by Tom Petty and the Heartbreakers featured on their early-1990s album.
-
D.
Killing Machine
Killing Machine is a film featuring American model and actress Margaux Hemingway in a prominent role.
-
E.
It Won't Kill Ya
"It Won't Kill Ya" is a dance-pop track by The Chainsmokers featuring Louane, known for its upbeat production and themes of taking risks in love.
- F. None of above. chosen
Provenance (5 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e328fe0f488190ac34aa677c980a20 |
completed | April 18, 2026, 6:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00458331748190a4bd1c5d2d466e6d |
completed | May 10, 2026, 8:44 a.m. |
| NEDg | Description generation | batch_6a0046ce23948190a2207e5e27493dac |
completed | May 10, 2026, 8:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a004767d1c88190814e83f09383e874 |
completed | May 10, 2026, 8:52 a.m. |
Created at: April 10, 2026, 5:09 a.m.