Triple
T1487463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | You & I (Nobody in the World) |
E29498
|
entity |
| Predicate | recordLabel |
P1500
|
FINISHED |
| Object | GOOD Music |
E5585
|
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: GOOD Music | Statement: [You & I (Nobody in the World), recordLabel, GOOD Music]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GOOD Music Context triple: [You & I (Nobody in the World), recordLabel, GOOD Music]
-
A.
GOOD Music
chosen
GOOD Music is a record label founded by Kanye West that is known for its roster of influential hip-hop and R&B artists.
-
B.
Good Vibes
Good Vibes is an animated television series known for its raunchy teen comedy set in a Southern California surf town.
-
C.
Gutes
The Gutes were the North Germanic inhabitants of the Baltic island of Gotland, known for their distinct Gutnish language and extensive Viking Age trade networks.
-
D.
MUS
MUS is the standard abbreviation used to refer to Museum station, a public transit stop.
-
E.
MUS
MUS is the three-letter ISO 3166-1 alpha-3 country code assigned to Mauritius for international identification and data standards.
- 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_69a498da82e08190ba833330d05f380f |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6a3325881909bbc55efc04ad60f |
completed | March 1, 2026, 11:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad1ca7804c8190b6818ff344908028 |
completed | March 8, 2026, 6:52 a.m. |
Created at: March 1, 2026, 8:12 p.m.