Triple
T13688020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caterina Valente |
E328183
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Malagueña
"Malagueña" is a popular song, originally a Spanish composition, that became widely known through numerous interpretations including a celebrated rendition by singer Caterina Valente.
|
E1054481
|
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: Malagueña | Statement: [Caterina Valente, notableWork, Malagueña]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malagueña Context triple: [Caterina Valente, notableWork, Malagueña]
-
A.
Manizaleña
Manizaleña is the Spanish term for a female inhabitant or native of the city of Manizales in Colombia.
-
B.
Candanchú
Candanchú is a historic ski resort in the Spanish Pyrenees, known for its alpine terrain and proximity to the French border.
-
C.
Malasaña
Malasaña is a vibrant central Madrid neighborhood known for its bohemian atmosphere, nightlife, and alternative cultural scene.
-
D.
Gurabeña
Gurabeña is the Spanish term for a female resident or native of the municipality of Gurabo in Puerto Rico.
-
E.
Canillejas
Canillejas is a Madrid Metro station serving the Canillejas neighborhood in the San Blas-Canillejas district of Madrid, Spain.
- 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: Malagueña Triple: [Caterina Valente, notableWork, Malagueña]
Generated description
"Malagueña" is a popular song, originally a Spanish composition, that became widely known through numerous interpretations including a celebrated rendition by singer Caterina Valente.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Malagueña Target entity description: "Malagueña" is a popular song, originally a Spanish composition, that became widely known through numerous interpretations including a celebrated rendition by singer Caterina Valente.
-
A.
Manizaleña
Manizaleña is the Spanish term for a female inhabitant or native of the city of Manizales in Colombia.
-
B.
Candanchú
Candanchú is a historic ski resort in the Spanish Pyrenees, known for its alpine terrain and proximity to the French border.
-
C.
Malasaña
Malasaña is a vibrant central Madrid neighborhood known for its bohemian atmosphere, nightlife, and alternative cultural scene.
-
D.
Gurabeña
Gurabeña is the Spanish term for a female resident or native of the municipality of Gurabo in Puerto Rico.
-
E.
Canillejas
Canillejas is a Madrid Metro station serving the Canillejas neighborhood in the San Blas-Canillejas district of Madrid, Spain.
- 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_69d8076ff62081908a7bd79889edd7a0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc670968881908e2b4fdf656c7285 |
completed | April 12, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7944981ec8190be5ff39b7c2c70ab |
completed | May 3, 2026, 6:30 p.m. |
| NEDg | Description generation | batch_69f795e361c48190b37060312e7df181 |
completed | May 3, 2026, 6:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f796e5c60c8190a19389bc4cdbd658 |
completed | May 3, 2026, 6:41 p.m. |
Created at: April 9, 2026, 9:53 p.m.