Triple
T17678733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Do You Know? |
E440710
|
entity |
| Predicate | hasTitle |
P38
|
FINISHED |
| Object | Do You Know? |
E440710
|
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: Do You Know? | Statement: [Do You Know?, hasTitle, Do You Know?]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Do You Know? Context triple: [Do You Know?, hasTitle, Do You Know?]
-
A.
Do You Know?
chosen
"Do You Know?" is a track featured on the album "No Way Out," associated with Sean "Diddy" Combs' late-1990s hip-hop and R&B sound.
-
B.
Do You Know
"Do You Know" is a contemporary gospel and R&B-influenced studio album by American singer Michelle Williams, showcasing her transition from Destiny’s Child member to solo artist.
-
C.
Do You Know
"Do You Know" is an R&B song written and produced by Manuel Seal, best known for his work with artists like Mariah Carey and Usher.
-
D.
Do You...
"Do You..." is a smooth, soulful R&B track by Miguel that blends sensual lyrics with atmospheric production and showcases his distinctive vocal style.
-
E.
What You Know
"What You Know" is a popular hip hop single by American rapper T.I., known for its heavy, cinematic production and chart success in the mid-2000s.
- 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46f6f8054819087b2fe9bc8ad8d2f |
completed | April 19, 2026, 6 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a022327dbf8819083cc9366ddcf3ed2 |
completed | May 11, 2026, 6:42 p.m. |
Created at: April 10, 2026, 10:01 a.m.