Triple
T3121175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lymari Nadal |
E65186
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Lymari Nadal |
E65186
|
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: Lymari Nadal | Statement: [Lymari Nadal, name, Lymari Nadal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lymari Nadal Context triple: [Lymari Nadal, name, Lymari Nadal]
-
A.
Lymari Nadal
chosen
Lymari Nadal is a Puerto Rican actress and producer best known for her role in the crime film "American Gangster."
-
B.
Marta Navarro
Marta Navarro is a personal name that may refer to multiple individuals across different fields, such as sports, arts, or public life, rather than a single widely recognized figure.
-
C.
María Navarro
María Navarro is a Spanish-language given name and surname combination shared by various notable figures in fields such as politics, academia, and the arts.
-
D.
Alba Flores
Alba Flores is a Spanish actress best known internationally for her role as Nairobi in the television series "Money Heist" (La Casa de Papel).
-
E.
Cristina Banegas
Cristina Banegas is an acclaimed Argentine actress and director recognized internationally for her powerful performances in film, television, and theater.
- 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_69ad857fcc088190b0c4d45a5cde6f61 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada5295cd481908d52e165538c67fa |
completed | March 8, 2026, 4:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b20f6bc644819093a7cab7220f4ca0 |
completed | March 12, 2026, 12:57 a.m. |
Created at: March 8, 2026, 3:04 p.m.