Triple
T4306469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queen Christina (1933 film) |
E99965
|
entity |
| Predicate | portraysCharacter |
P1668
|
FINISHED |
| Object |
Don Antonio
Don Antonio is a fictional character featured in the 1933 historical drama film "Queen Christina," set in 17th-century Sweden.
|
E429161
|
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: Don Antonio | Statement: [Queen Christina (1933 film), portraysCharacter, Don Antonio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Don Antonio Context triple: [Queen Christina (1933 film), portraysCharacter, Don Antonio]
-
A.
Alfonso
Alfonso is a masculine given name of Spanish and Italian origin historically borne by numerous kings, nobles, and notable figures across Europe.
-
B.
Federico
Federico is the Italian and Spanish form of the given name Frederick, commonly used in Romance-language countries.
-
C.
Fernando
Fernando is the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
-
D.
Fernando
"Fernando" is a popular 1976 ballad by Swedish pop group ABBA, known for its nostalgic, storytelling lyrics and melodic harmonies.
-
E.
Fernando
Fernando was the given name of the Duke of Alba who served as governor-general, a prominent Spanish noble and military leader.
- 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: Don Antonio Triple: [Queen Christina (1933 film), portraysCharacter, Don Antonio]
Generated description
Don Antonio is a fictional character featured in the 1933 historical drama film "Queen Christina," set in 17th-century Sweden.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Don Antonio Target entity description: Don Antonio is a fictional character featured in the 1933 historical drama film "Queen Christina," set in 17th-century Sweden.
-
A.
Alfonso
Alfonso is a masculine given name of Spanish and Italian origin historically borne by numerous kings, nobles, and notable figures across Europe.
-
B.
Federico
Federico is the Italian and Spanish form of the given name Frederick, commonly used in Romance-language countries.
-
C.
Fernando
Fernando is the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
-
D.
Fernando
"Fernando" is a popular 1976 ballad by Swedish pop group ABBA, known for its nostalgic, storytelling lyrics and melodic harmonies.
-
E.
Fernando
Fernando was the given name of the Duke of Alba who served as governor-general, a prominent Spanish noble and military leader.
- 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_69b345528ebc8190b5abc7e95094792d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b350bb78cc8190a850aca47d8711cf |
completed | March 12, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c756809c8190af90c91ec7883e55 |
completed | March 14, 2026, 8:38 p.m. |
| NEDg | Description generation | batch_69b5c804f3f881908dd2d020d07c4859 |
completed | March 14, 2026, 8:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5c84e48b8819080571f995d8baf13 |
completed | March 14, 2026, 8:42 p.m. |
Created at: March 12, 2026, 11:09 p.m.