Triple
T2641685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Rolling Stones |
E62881
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Brown Sugar |
E147773
|
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: Brown Sugar | Statement: [The Rolling Stones, notableWork, Brown Sugar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brown Sugar Context triple: [The Rolling Stones, notableWork, Brown Sugar]
-
A.
Brown Sugar
chosen
"Brown Sugar" is a 1971 rock song by the Rolling Stones, known for its gritty guitar riff, controversial lyrics, and status as one of the band’s signature hits.
-
B.
Brown Sugar
Brown Sugar is a 2002 romantic comedy-drama film about lifelong friends navigating love and hip-hop in New York City, starring Taye Diggs and Sanaa Lathan.
-
C.
Sweetener
Sweetener is Ariana Grande's critically acclaimed fourth studio album, noted for its blend of pop and R&B with innovative production and themes of healing and empowerment.
-
D.
Watermelon Sugar
"Watermelon Sugar" is a hit pop song by English singer Harry Styles, known for its summery sound and widespread commercial success.
-
E.
Sugar
Sugar is a child-friendly, open-source learning platform and graphical interface designed to support education on low-cost laptops like those from the One Laptop per Child project.
- 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_69ab4c3f2dcc819082df80f5e032f690 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abd8fdc0bc8190b7fd102b87ee50d1 |
completed | March 7, 2026, 7:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af98bfd4008190a30675ebaf01e483 |
completed | March 10, 2026, 4:06 a.m. |
Created at: March 6, 2026, 9:53 p.m.