Triple
T3563534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tha Blue Carpet Treatment |
E75391
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Nottz |
E65307
|
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: Nottz | Statement: [Tha Blue Carpet Treatment, producer, Nottz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nottz Context triple: [Tha Blue Carpet Treatment, producer, Nottz]
-
A.
Nottz
chosen
Nottz is an American hip-hop producer known for his soulful, sample-based beats and work with artists such as Kanye West, Busta Rhymes, and Snoop Dogg.
-
B.
Notsi
Notsi is an Oceanic language spoken in parts of Papua New Guinea, belonging to the Meso-Melanesian branch of the Austronesian language family.
-
C.
Y-Not
Y-Not is a music producer best known for working on the influential hip-hop album "Electric Circus."
-
D.
Beyton
Beyton is a small rural village and civil parish in the English county of Suffolk, known for its traditional village green and historic buildings.
-
E.
Snot Lonstein
Snot Lonstein is a fictional character from the animated TV series "American Dad!", known as one of Steve Smith's nerdy best friends.
- 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_69ad85d512708190829c8b2d3a2ccfb8 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0a60e6c8190a3c3ddae5b6ded54 |
completed | March 8, 2026, 6:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bba5ccf8819090027c445a1fd458 |
completed | March 13, 2026, 7:24 a.m. |
Created at: March 8, 2026, 3:21 p.m.