Triple
T11996365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amores perros |
E285539
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
Goya Toledo
Goya Toledo is a Spanish actress and former model best known internationally for her role in the acclaimed film "Amores perros."
|
E1040114
|
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: Goya Toledo | Statement: [Amores perros, stars, Goya Toledo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Goya Toledo Context triple: [Amores perros, stars, Goya Toledo]
-
A.
Francisco Goya
Francisco Goya was a pioneering Spanish Romantic painter and printmaker renowned for his powerful portraits, dark and haunting imagery, and critical depictions of war and society.
-
B.
Goya
Goya is Habana Labs’ AI inference processor designed to accelerate deep learning workloads with high efficiency and scalability.
-
C.
Zurbarán
Zurbarán was a 17th-century Spanish Baroque painter renowned for his starkly realistic religious scenes and masterful use of chiaroscuro.
-
D.
Jusepe de Ribera
Jusepe de Ribera was a 17th-century Spanish Tenebrist painter and printmaker, renowned for his dramatic use of light and shadow and intense religious and mythological scenes.
-
E.
Velásquez
Velásquez is a Spanish-language surname common in Hispanic countries and among people of Spanish or Latin American heritage.
- 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: Goya Toledo Triple: [Amores perros, stars, Goya Toledo]
Generated description
Goya Toledo is a Spanish actress and former model best known internationally for her role in the acclaimed film "Amores perros."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Goya Toledo Target entity description: Goya Toledo is a Spanish actress and former model best known internationally for her role in the acclaimed film "Amores perros."
-
A.
Francisco Goya
Francisco Goya was a pioneering Spanish Romantic painter and printmaker renowned for his powerful portraits, dark and haunting imagery, and critical depictions of war and society.
-
B.
Goya
Goya is Habana Labs’ AI inference processor designed to accelerate deep learning workloads with high efficiency and scalability.
-
C.
Zurbarán
Zurbarán was a 17th-century Spanish Baroque painter renowned for his starkly realistic religious scenes and masterful use of chiaroscuro.
-
D.
Jusepe de Ribera
Jusepe de Ribera was a 17th-century Spanish Tenebrist painter and printmaker, renowned for his dramatic use of light and shadow and intense religious and mythological scenes.
-
E.
Velásquez
Velásquez is a Spanish-language surname common in Hispanic countries and among people of Spanish or Latin American heritage.
- 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903c172788190b92042e9d10a48bf |
completed | April 10, 2026, 2:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f739644ed48190896d7b2b6c3b5f8f |
completed | May 3, 2026, 12:02 p.m. |
| NEDg | Description generation | batch_69f73add05c881908e65cb157a0a5fc3 |
completed | May 3, 2026, 12:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f73b434a0881909f1f75cad643cb70 |
completed | May 3, 2026, 12:10 p.m. |
Created at: April 8, 2026, 9:46 p.m.