Triple
T3996081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Good Girl Gone Bad |
E87100
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Syience |
E249513
|
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: Syience | Statement: [Good Girl Gone Bad, producer, Syience]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Syience Context triple: [Good Girl Gone Bad, producer, Syience]
-
A.
Syience
chosen
Syience is a music producer known for his work in contemporary R&B and pop, collaborating with prominent artists on charting tracks.
-
B.
SCI
SCI is the abbreviation for the Strategic Computing Initiative, a U.S. Defense Advanced Research Projects Agency (DARPA) program from the 1980s that aimed to advance artificial intelligence and high-performance computing for military applications.
-
C.
SCI
SCI is a widely used citation indexing service that tracks and analyzes references in leading scientific journals to assess research impact and influence.
-
D.
Science
Science is a leading peer-reviewed academic journal that publishes cutting-edge research across a wide range of scientific disciplines.
-
E.
Sayanci
Sayanci is a West Chadic language spoken in parts of northern Nigeria.
- 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_69aed94118148190975e6aa4e554cde9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefa2159d88190a01de8b038341916 |
completed | March 9, 2026, 4:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5403f14ec8190a77189c7066676f2 |
completed | March 14, 2026, 11:02 a.m. |
Created at: March 9, 2026, 3:34 p.m.