Triple
T23546540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angela Beyincé |
E577910
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Naughty Girl |
—
|
NE NERFINISHED |
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: Naughty Girl | Statement: [Angela Beyincé, notableWork, Naughty Girl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Naughty Girl Context triple: [Angela Beyincé, notableWork, Naughty Girl]
-
A.
Naughty Girl
chosen
"Naughty Girl" is a hit R&B song by Beyoncé from her debut solo album, noted for its sultry vocals and Middle Eastern–influenced production.
-
B.
Bad Girl
"Bad Girl" is a high-energy R&B/pop single by Danity Kane known for its club-ready production and confident, empowering lyrics.
-
C.
Bad Girl
"Bad Girl" is a track from the album "Erotica," known for its provocative themes and sensual style.
-
D.
Bad Girl
"Bad Girl" is a 1931 American pre-Code romantic drama film, directed by Frank Borzage and acclaimed for its realistic portrayal of working-class marriage and its Academy Award–winning achievements.
-
E.
Naughty Baby
"Naughty Baby" is a playful, flirtatious song from the George and Ira Gershwin musical *Crazy for You*.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245f9d5d08190a4a20004e1784e20 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1aecb567c8190a54d2c3b63282af5 |
completed | April 29, 2026, 7:10 a.m. |
Created at: April 17, 2026, 6:11 p.m.