Triple
T16678535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luvanmusiq |
E405276
|
entity |
| Predicate | hasTrack |
P3284
|
FINISHED |
| Object | Makeyouhappy |
E1228126
|
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: Makeyouhappy | Statement: [Luvanmusiq, hasTrack, Makeyouhappy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Makeyouhappy Context triple: [Luvanmusiq, hasTrack, Makeyouhappy]
-
A.
Makeyouhappy
chosen
"Makeyouhappy" is likely a song title, possibly a follow-up track in a music release sequence.
-
B.
Make You Happy
"Make You Happy" is a song featured on Céline Dion’s 1996 album *Falling into You*.
-
C.
Make You Happy
"Make You Happy" is a song featured on the album *The Origin of Love* by French singer-songwriter Mika.
-
D.
Make Someone Happy
"Make Someone Happy" is a jazz album by Russian-Canadian vocalist Sophie Milman, showcasing her interpretations of classic standards.
-
E.
Whatever Makes You Happy
"Whatever Makes You Happy" is a novel by William Sutcliffe that follows three over-involved mothers who secretly track down their adult sons in New York, blending humor and insight about family, independence, and modern parent-child relationships.
- 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_69d8838c28748190b3f5967c743940ab |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37d6db3488190b56e13ddb69ef8f1 |
completed | April 18, 2026, 12:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a009d32e7b48190b7dd4660bed4789d |
completed | May 10, 2026, 2:58 p.m. |
Created at: April 10, 2026, 5:19 a.m.