Triple
T1202728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Have One on Me |
E25818
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Noah Georgeson |
E99963
|
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: Noah Georgeson | Statement: [Have One on Me, producer, Noah Georgeson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Noah Georgeson Context triple: [Have One on Me, producer, Noah Georgeson]
-
A.
Noah Georgeson
chosen
Noah Georgeson is an American record producer, musician, and mixer known for his work with indie and folk artists such as Joanna Newsom and Devendra Banhart.
-
B.
Noah Glass
Noah Glass is an American software developer and entrepreneur best known as one of the early co-founders who helped create and shape Twitter.
-
C.
Noah Shebib
Noah "40" Shebib is a Canadian record producer, songwriter, and audio engineer best known for his long-standing collaboration with Drake and his influential, atmospheric production style in contemporary hip hop and R&B.
-
D.
Noah Johnston
Noah Johnston is a voice actor best known for providing the voice of young Mike Wazowski in Pixar's animated film "Monsters University."
-
E.
Chris Larson
Chris Larson is an alumnus of the prestigious Lakeside School in Seattle, known for producing many notable graduates in business, technology, and public life.
- 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_69a4942b30f08190a91c60573e16b5ef |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bdbda0b081909c0147121a945e27 |
completed | March 1, 2026, 10:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acacaf11588190b0bbc1d280bf9a78 |
completed | March 7, 2026, 10:54 p.m. |
Created at: March 1, 2026, 7:46 p.m.