Triple
T14767720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miguel Gomez |
E347041
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Miguel Gomez |
E347041
|
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 Gomez | Statement: [Miguel Gomez, name, Miguel Gomez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miguel Gomez Context triple: [Miguel Gomez, name, Miguel Gomez]
-
A.
Miguel Gomez
chosen
Miguel Gomez is an American actor best known for his role as boxer Miguel "Magic" Escobar in the 2015 sports drama film "Southpaw."
-
B.
Bruno Bichir
Bruno Bichir is a Mexican actor known for his work in film, television, and theater, and as a member of the prominent Bichir acting family.
-
C.
Emilio Pérez Touriño
Emilio Pérez Touriño is a Spanish economist and politician who served as president of the autonomous community of Galicia.
-
D.
Luis Krahl
Luis Krahl is a mountaineer known for making the first recorded ascent of Cerro San Valentín, the highest peak in Chilean Patagonia.
-
E.
Álvaro Morte
Álvaro Morte is a Spanish actor best known internationally for portraying "The Professor" in the hit series *Money Heist* (La Casa de Papel).
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec81236f081908063bb4350b7b985 |
completed | April 14, 2026, 11:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe0cf86730819082cf3f502ec16a46 |
completed | May 8, 2026, 4:19 p.m. |
Created at: April 10, 2026, 1:30 a.m.