Triple
T13993599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Euphoria |
E336639
|
entity |
| Predicate | notableTrack |
P8087
|
FINISHED |
| Object | Cuando Me Enamoro |
E502797
|
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: Cuando Me Enamoro | Statement: [Euphoria, notableTrack, Cuando Me Enamoro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cuando Me Enamoro Context triple: [Euphoria, notableTrack, Cuando Me Enamoro]
-
A.
Cuando me enamoro
chosen
"Cuando me enamoro" is a Mexican telenovela best known for its romantic drama storyline and the starring performance of actress Silvia Navarro.
-
B.
Si No Te Quiere
"Si No Te Quiere" is a breakthrough reggaeton/Latin trap song by Puerto Rican artist Ozuna that helped launch him to international fame.
-
C.
Te Amo
"Te Amo" is a romantic song featured on the soundtrack of the film "Rated R."
-
D.
Dile Que Tú Me Quieres
"Dile Que Tú Me Quieres" is a popular reggaeton song by Puerto Rican artist Ozuna that helped propel his rise in the Latin urban music scene.
-
E.
Tú Sí Sabes Quererme
"Tú Sí Sabes Quererme" is a romantic, folk-infused song by Mexican singer-songwriter Natalia Lafourcade that blends traditional Latin American sounds with contemporary pop.
- 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_69d81c639e808190a0e4b4f3d31c6a59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2eb3b5d881909f15a1e08bb202f3 |
completed | April 14, 2026, 12:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbc32789e08190be0f1d1685dcc90e |
completed | May 6, 2026, 10:39 p.m. |
Created at: April 9, 2026, 10:19 p.m.