Triple
T9166848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Bunny |
E219980
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Mía |
E429966
|
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: Mía | Statement: [Bad Bunny, notableWork, Mía]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mía Context triple: [Bad Bunny, notableWork, Mía]
-
A.
Mia
Mia is a major fine art museum in Minneapolis, Minnesota, known for its extensive and diverse collection spanning thousands of years and cultures.
-
B.
Mia
chosen
Mia is a feminine given name used in many cultures, often as a short form of names like Maria or Amelia.
-
C.
Niña
Niña was one of the three ships in Christopher Columbus’s 1492 voyage across the Atlantic, notable for its role in the first European expedition to the Americas.
-
D.
Miguel
Miguel is an American R&B singer, songwriter, and producer known for his smooth vocals and genre-blending, atmospheric sound.
-
E.
Miguel
Miguel is a Spanish given name widely used in the Hispanic world, notably borne by figures such as Mexican independence leader Miguel Hidalgo y Costilla.
- 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_69ca83e467108190abcae6a33b3d4dad |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccaade47cc81909b5c127dc8aa1340 |
completed | April 1, 2026, 5:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0548b0e6c819090ece33b9ede9fa6 |
completed | April 4, 2026, midnight |
Created at: March 30, 2026, 7:22 p.m.