Triple
T1812378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ozuna |
E40358
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Aura
Aura is a popular reggaeton and Latin trap album by Puerto Rican singer Ozuna, known for its melodic style and chart-topping hits.
|
E200802
|
NE FINISHED |
How this triple was built (4 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: Aura | Statement: [Ozuna, notableWork, Aura]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aura Context triple: [Ozuna, notableWork, Aura]
-
A.
Aria
Aria is a crash-safe, transactional storage engine used in MariaDB for efficient handling of complex queries and temporary tables.
-
B.
Dani Ardor
Dani Ardor is the emotionally traumatized American graduate student who becomes entangled with a sinister Swedish pagan cult in Ari Aster’s 2019 folk horror film "Midsommar."
-
C.
URA
URA (Universities Research Association) is a consortium of research universities that collaborates to advance high-energy physics and other scientific research through managing and supporting major research facilities and projects.
-
D.
Ale
Ale is a common short form of the Italian given name Alessandro, often used as a casual or affectionate nickname.
-
E.
Annabella
Annabella was a French film actress of the 1930s and 1940s, known for her work in both European and Hollywood cinema.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Aura Triple: [Ozuna, notableWork, Aura]
Generated description
Aura is a popular reggaeton and Latin trap album by Puerto Rican singer Ozuna, known for its melodic style and chart-topping hits.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Aura Target entity description: Aura is a popular reggaeton and Latin trap album by Puerto Rican singer Ozuna, known for its melodic style and chart-topping hits.
-
A.
Aria
Aria is a crash-safe, transactional storage engine used in MariaDB for efficient handling of complex queries and temporary tables.
-
B.
Dani Ardor
Dani Ardor is the emotionally traumatized American graduate student who becomes entangled with a sinister Swedish pagan cult in Ari Aster’s 2019 folk horror film "Midsommar."
-
C.
URA
URA (Universities Research Association) is a consortium of research universities that collaborates to advance high-energy physics and other scientific research through managing and supporting major research facilities and projects.
-
D.
Ale
Ale is a common short form of the Italian given name Alessandro, often used as a casual or affectionate nickname.
-
E.
Annabella
Annabella was a French film actress of the 1930s and 1940s, known for her work in both European and Hollywood cinema.
- F. None of above. chosen
Provenance (5 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_69a88643a3388190a612f2ebe1fb29e7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa65c775408190b4f5912786720e28 |
completed | March 6, 2026, 5:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adb5e5142c8190bc90da38b02e95e2 |
completed | March 8, 2026, 5:46 p.m. |
| NEDg | Description generation | batch_69adb69d10188190b78bece656249ecd |
completed | March 8, 2026, 5:49 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adb8c122e881908f0640edc5aaf305 |
completed | March 8, 2026, 5:58 p.m. |
Created at: March 4, 2026, 7:32 p.m.