Triple
T11859709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legally Blonde (Broadway musical) |
E282128
|
entity |
| Predicate | featuresSong |
P2152
|
FINISHED |
| Object | What You Want |
E451602
|
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: What You Want | Statement: [Legally Blonde (Broadway musical), featuresSong, What You Want]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: What You Want Context triple: [Legally Blonde (Broadway musical), featuresSong, What You Want]
-
A.
What You Want
chosen
"What You Want" is a hip hop track by Mase from his debut album *Harlem World*, showcasing his smooth flow and late-1990s Bad Boy Records sound.
-
B.
What More Do You Want
"What More Do You Want" is a song featured on the album *Some Lessons Learned* by Kristin Chenoweth.
-
C.
Exactly What You Wanted
"Exactly What You Wanted" is a popular alternative metal song by the American band Helmet, known for its tight, riff-driven sound and dynamic shifts.
-
D.
If You Want It
"If You Want It" is a song by Lenny Kravitz featured on his 2008 album *It Is Time for a Love Revolution*.
-
E.
Whatever You Want
"Whatever You Want" is a smooth R&B ballad by Tony! Toni! Toné! that became one of the group's signature hits in the early 1990s.
- 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_69d6ab287ba48190a5178779fd19b9b7 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a69a099c8190a674db64c50eca5a |
completed | April 10, 2026, 7:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f281745ca88190968e1f674d0e483c |
completed | April 29, 2026, 10:08 p.m. |
Created at: April 8, 2026, 9:43 p.m.