Triple
T5030954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kristīne Opolais |
E113299
|
entity |
| Predicate | notableRole |
P22
|
FINISHED |
| Object |
Cio-Cio San
Cio-Cio San is the tragic Japanese geisha heroine of Giacomo Puccini’s opera "Madama Butterfly."
|
E487517
|
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: Cio-Cio San | Statement: [Kristīne Opolais, notableRole, Cio-Cio San]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cio-Cio San Context triple: [Kristīne Opolais, notableRole, Cio-Cio San]
-
A.
Shigeko
Shigeko is a Japanese feminine given name that has been borne by various notable women, including members of the imperial family.
-
B.
Yorimichi
Yorimichi is a Japanese given name most notably borne by the powerful Heian-period court noble Fujiwara no Yorimichi.
-
C.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
-
D.
Ono no Komachi
Ono no Komachi was a renowned 9th-century Japanese waka poet celebrated for her passionate verse and legendary beauty, and is counted among the Rokkasen and Thirty-Six Immortals of Poetry.
-
E.
Princess Toshiko
Princess Toshiko was a Japanese imperial princess of the Higashikuni-no-miya branch of the Imperial Family, known for her role within early 20th-century Japanese court society.
- 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: Cio-Cio San Triple: [Kristīne Opolais, notableRole, Cio-Cio San]
Generated description
Cio-Cio San is the tragic Japanese geisha heroine of Giacomo Puccini’s opera "Madama Butterfly."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cio-Cio San Target entity description: Cio-Cio San is the tragic Japanese geisha heroine of Giacomo Puccini’s opera "Madama Butterfly."
-
A.
Shigeko
Shigeko is a Japanese feminine given name that has been borne by various notable women, including members of the imperial family.
-
B.
Yorimichi
Yorimichi is a Japanese given name most notably borne by the powerful Heian-period court noble Fujiwara no Yorimichi.
-
C.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
-
D.
Ono no Komachi
Ono no Komachi was a renowned 9th-century Japanese waka poet celebrated for her passionate verse and legendary beauty, and is counted among the Rokkasen and Thirty-Six Immortals of Poetry.
-
E.
Princess Toshiko
Princess Toshiko was a Japanese imperial princess of the Higashikuni-no-miya branch of the Imperial Family, known for her role within early 20th-century Japanese court society.
- 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_69bd443775e48190a646ffbfc4334723 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73922a4c81908651c2d9b5e01cb6 |
completed | March 20, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be9c6df1b88190ad61c87a28312957 |
completed | March 21, 2026, 1:26 p.m. |
| NEDg | Description generation | batch_69be9d0b87d48190a2f75e7c6e472d94 |
completed | March 21, 2026, 1:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69be9d6b81888190b203988b37306df2 |
completed | March 21, 2026, 1:30 p.m. |
Created at: March 20, 2026, 1:36 p.m.