Triple
T14610710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | All Hail the Queen |
E342952
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Daddy-O |
E332364
|
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: Daddy-O | Statement: [All Hail the Queen, producer, Daddy-O]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daddy-O Context triple: [All Hail the Queen, producer, Daddy-O]
-
A.
Daddy-O
chosen
Daddy-O is an American rapper and producer best known as a founding member of the influential hip hop group Stetsasonic.
-
B.
Daddy Pop
"Daddy Pop" is a funk-infused pop song by Prince and the New Power Generation from the early 1990s.
-
C.
Daddy
"Daddy" is a song featured on the album *Wanderland* by American singer-songwriter Kelis.
-
D.
Daddy
"Daddy" is a powerful and controversial confessional poem by Sylvia Plath that explores themes of trauma, oppression, and the speaker’s fraught relationship with her father.
-
E.
Daddy
"Daddy" is a critically acclaimed 1989 Indian drama film featuring Anupam Kher in one of his most celebrated roles, portraying an alcoholic father seeking redemption and reconciliation with his daughter.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb44f0dd48190a78662b5998a6722 |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fda91f437c8190ada4d1c3708faedd |
completed | May 8, 2026, 9:13 a.m. |
Created at: April 10, 2026, 1:25 a.m.