Triple
T3996080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Good Girl Gone Bad |
E87100
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | J.R. Rotem |
E344626
|
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: J.R. Rotem | Statement: [Good Girl Gone Bad, producer, J.R. Rotem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: J.R. Rotem Context triple: [Good Girl Gone Bad, producer, J.R. Rotem]
-
A.
J.R. Rotem
chosen
J.R. Rotem is a South African-born American record producer and songwriter known for crafting pop and hip-hop hits for artists such as Rihanna, Jason Derulo, and Sean Kingston.
-
B.
Harel Weinstein
Harel Weinstein is an Israeli-American neuroscientist and biophysicist known for his work on membrane proteins and computational neuroscience.
-
C.
Mark Shtaif
Mark Shtaif is an Israeli electrical engineer and academic who serves as rector of Tel Aviv University and is known for his research in optical communications and photonics.
-
D.
Amir Shinar
Amir Shinar is an Israeli entrepreneur and software engineer best known as one of the co-founders of the GPS navigation and traffic app Waze.
-
E.
Avi Lerner
Avi Lerner is an Israeli-American film producer and founder of Millennium Films, known for financing and producing numerous action movies and franchises.
- 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.