Triple
T14547381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miguel Barnet |
E341323
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Miguel |
E95446
|
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: Miguel | Statement: [Miguel Barnet, givenName, Miguel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miguel Context triple: [Miguel Barnet, givenName, Miguel]
-
A.
Miguel
Miguel is an American R&B singer, songwriter, and producer known for his smooth vocals and genre-blending, atmospheric sound.
-
B.
Miguel
chosen
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.
-
C.
Niño
Niño is a Spanish surname commonly borne by individuals and families in Spanish-speaking countries.
-
D.
Rodrigo
Rodrigo is a masculine given name of Spanish and Portuguese origin, derived from the Germanic name Roderick and commonly used across the Spanish-speaking world.
-
E.
Luis
Luis is a friendly human character on Sesame Street who often interacts warmly with Big Bird and the other residents of the neighborhood.
- 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_69d822db9c8481908213ceb39585f792 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb2ebdf9481909f4d2da1ad31099c |
completed | April 14, 2026, 9:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd7a6344b08190a3c1124c6dd7da96 |
completed | May 8, 2026, 5:53 a.m. |
Created at: April 10, 2026, 1:23 a.m.