Triple
T10563488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wifredo Lam |
E249287
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Helena Benítez
Helena Benítez was the spouse of renowned Cuban modernist painter Wifredo Lam.
|
E893061
|
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: Helena Benítez | Statement: [Wifredo Lam, spouse, Helena Benítez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Helena Benítez Context triple: [Wifredo Lam, spouse, Helena Benítez]
-
A.
Miriam Gómez
Miriam Gómez is a Cuban actress and writer best known as the longtime partner and literary collaborator of novelist Guillermo Cabrera Infante.
-
B.
Cristina Banegas
Cristina Banegas is an acclaimed Argentine actress and director recognized internationally for her powerful performances in film, television, and theater.
-
C.
Olga Carmona
Olga Carmona is a Spanish professional footballer, primarily a left-back, known for her key role and decisive goals with both Real Madrid Femenino and the Spain women's national team.
-
D.
Esther Fernández
Esther Fernández was a prominent Mexican film actress known for her work during the Golden Age of Mexican cinema.
-
E.
Elena García
Elena García is a common Spanish personal name shared by multiple notable individuals across fields such as science, arts, and public life.
- 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: Helena Benítez Triple: [Wifredo Lam, spouse, Helena Benítez]
Generated description
Helena Benítez was the spouse of renowned Cuban modernist painter Wifredo Lam.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Helena Benítez Target entity description: Helena Benítez was the spouse of renowned Cuban modernist painter Wifredo Lam.
-
A.
Miriam Gómez
Miriam Gómez is a Cuban actress and writer best known as the longtime partner and literary collaborator of novelist Guillermo Cabrera Infante.
-
B.
Cristina Banegas
Cristina Banegas is an acclaimed Argentine actress and director recognized internationally for her powerful performances in film, television, and theater.
-
C.
Olga Carmona
Olga Carmona is a Spanish professional footballer, primarily a left-back, known for her key role and decisive goals with both Real Madrid Femenino and the Spain women's national team.
-
D.
Esther Fernández
Esther Fernández was a prominent Mexican film actress known for her work during the Golden Age of Mexican cinema.
-
E.
Elena García
Elena García is a common Spanish personal name shared by multiple notable individuals across fields such as science, arts, and public life.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d527224b808190b996ae970393f9c3 |
completed | April 7, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e215e3c3c88190833c1f56288629a2 |
completed | April 17, 2026, 11:13 a.m. |
| NEDg | Description generation | batch_69e21d860d288190855ffbe60df50df9 |
completed | April 17, 2026, 11:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e21f09be508190a7c497a7680cb59e |
completed | April 17, 2026, 11:52 a.m. |
Created at: April 6, 2026, 12:36 p.m.