Triple
T3286472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antonio Banderas |
E68992
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Ana Leza
Ana Leza is a Spanish actress best known for her work in film and television in the 1980s and 1990s and for her former marriage to actor Antonio Banderas.
|
E344158
|
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: Ana Leza | Statement: [Antonio Banderas, spouse, Ana Leza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ana Leza Context triple: [Antonio Banderas, spouse, Ana Leza]
-
A.
Luciana
Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
-
B.
Alejandra
Alejandra is the feminine given name corresponding to Alejandro, commonly used in Spanish-speaking cultures.
-
C.
Romina
Romina is an Italian-American actress and singer best known as half of the pop duo Al Bano & Romina Power.
-
D.
Ticha Penicheiro
Ticha Penicheiro is a Portuguese former professional basketball player and WNBA star renowned as one of the greatest passers and playmakers in women’s basketball history.
-
E.
Paola
Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
- 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: Ana Leza Triple: [Antonio Banderas, spouse, Ana Leza]
Generated description
Ana Leza is a Spanish actress best known for her work in film and television in the 1980s and 1990s and for her former marriage to actor Antonio Banderas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ana Leza Target entity description: Ana Leza is a Spanish actress best known for her work in film and television in the 1980s and 1990s and for her former marriage to actor Antonio Banderas.
-
A.
Luciana
Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
-
B.
Alejandra
Alejandra is the feminine given name corresponding to Alejandro, commonly used in Spanish-speaking cultures.
-
C.
Romina
Romina is an Italian-American actress and singer best known as half of the pop duo Al Bano & Romina Power.
-
D.
Ticha Penicheiro
Ticha Penicheiro is a Portuguese former professional basketball player and WNBA star renowned as one of the greatest passers and playmakers in women’s basketball history.
-
E.
Paola
Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
- 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_69ad859d45748190b0742408c954b39f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb05779d08190a5517951e71b1380 |
completed | March 8, 2026, 5:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2e85b6a1081908581b2040b8ce261 |
completed | March 12, 2026, 4:22 p.m. |
| NEDg | Description generation | batch_69b2e9783918819085ea73a8ed815f65 |
completed | March 12, 2026, 4:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2ea0189948190b1dab60aecf04b73 |
completed | March 12, 2026, 4:29 p.m. |
Created at: March 8, 2026, 3:10 p.m.