Triple
T10660680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pilar Bardem |
E251214
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object |
Mónica Bardem
Mónica Bardem is a Spanish actress and member of the Bardem family, known for her work in film and television.
|
E881458
|
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: Mónica Bardem | Statement: [Pilar Bardem, relative, Mónica Bardem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mónica Bardem Context triple: [Pilar Bardem, relative, Mónica Bardem]
-
A.
Pilar Bardem
Pilar Bardem was a Spanish actress and prominent member of the Bardem acting family, known for her extensive film and television career and her activism.
-
B.
Paz Vega
Paz Vega is a Spanish actress known for her roles in films such as "Sex and Lucía," "Spanglish," and various international productions.
-
C.
Emilia Gorriarán
Emilia Gorriarán was the mother of Cuban revolutionary figure Camilo Cienfuegos.
-
D.
Ángela Molina
Ángela Molina is a renowned Spanish actress known for her work in European art-house cinema and collaborations with prominent directors such as Luis Buñuel.
-
E.
Maribel Verdú
Maribel Verdú is a Spanish actress acclaimed for her work in films such as "Pan’s Labyrinth" and "Y Tu Mamá También."
- 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: Mónica Bardem Triple: [Pilar Bardem, relative, Mónica Bardem]
Generated description
Mónica Bardem is a Spanish actress and member of the Bardem family, known for her work in film and television.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mónica Bardem Target entity description: Mónica Bardem is a Spanish actress and member of the Bardem family, known for her work in film and television.
-
A.
Pilar Bardem
Pilar Bardem was a Spanish actress and prominent member of the Bardem acting family, known for her extensive film and television career and her activism.
-
B.
Paz Vega
Paz Vega is a Spanish actress known for her roles in films such as "Sex and Lucía," "Spanglish," and various international productions.
-
C.
Emilia Gorriarán
Emilia Gorriarán was the mother of Cuban revolutionary figure Camilo Cienfuegos.
-
D.
Ángela Molina
Ángela Molina is a renowned Spanish actress known for her work in European art-house cinema and collaborations with prominent directors such as Luis Buñuel.
-
E.
Maribel Verdú
Maribel Verdú is a Spanish actress acclaimed for her work in films such as "Pan’s Labyrinth" and "Y Tu Mamá También."
- 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_69d6aa5b0d2881909584b20efc5877f0 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6e017f97c8190b22765a6f1e6719d |
completed | April 8, 2026, 11:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dbace2a5388190bb685d347dd8aa6c |
completed | April 12, 2026, 2:32 p.m. |
| NEDg | Description generation | batch_69dbaeb211088190a9118c71918584e5 |
completed | April 12, 2026, 2:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69dbaf7c999c819097a8cdf5bd82f648 |
completed | April 12, 2026, 2:43 p.m. |
Created at: April 8, 2026, 9:08 p.m.