Triple
T17436423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dreams |
E424011
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object |
Tome Minami
Tome Minami is a Japanese manga editor best known for her work on the series "Dreams."
|
E1268730
|
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: Tome Minami | Statement: [Dreams, editor, Tome Minami]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tome Minami Context triple: [Dreams, editor, Tome Minami]
-
A.
Takanami
Takanami was a Japanese destroyer of the Imperial Japanese Navy during World War II, notable for being sunk in the Battle of Tassafaronga in 1942.
-
B.
Kodama Masashi
Kodama Masashi is a Japanese individual notable for bearing the surname Kodama, though specific widely recognized public achievements or roles under this name are not well documented in major English-language sources.
-
C.
Nishi Amane
Nishi Amane was a pioneering Meiji-era Japanese philosopher and statesman who helped introduce Western philosophy and legal thought to Japan.
-
D.
Miko Mayama
Miko Mayama is a Japanese-American actress best known for her film and television roles in the 1960s and 1970s.
-
E.
Kodama Kyūichi
Kodama Kyūichi was a Japanese politician and bureaucrat who served in several high-ranking government and administrative posts in the early 20th century.
- 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: Tome Minami Triple: [Dreams, editor, Tome Minami]
Generated description
Tome Minami is a Japanese manga editor best known for her work on the series "Dreams."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tome Minami Target entity description: Tome Minami is a Japanese manga editor best known for her work on the series "Dreams."
-
A.
Takanami
Takanami was a Japanese destroyer of the Imperial Japanese Navy during World War II, notable for being sunk in the Battle of Tassafaronga in 1942.
-
B.
Kodama Masashi
Kodama Masashi is a Japanese individual notable for bearing the surname Kodama, though specific widely recognized public achievements or roles under this name are not well documented in major English-language sources.
-
C.
Nishi Amane
Nishi Amane was a pioneering Meiji-era Japanese philosopher and statesman who helped introduce Western philosophy and legal thought to Japan.
-
D.
Miko Mayama
Miko Mayama is a Japanese-American actress best known for her film and television roles in the 1960s and 1970s.
-
E.
Kodama Kyūichi
Kodama Kyūichi was a Japanese politician and bureaucrat who served in several high-ranking government and administrative posts in the early 20th century.
- 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_69d889d88b6081908bada047f5b3ba51 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4490426008190b474ed76aca5d6f3 |
completed | April 19, 2026, 3:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01aff0b26c8190a6e65451e52199bb |
completed | May 11, 2026, 10:31 a.m. |
| NEDg | Description generation | batch_6a01b12b5df4819089fd89fd25338533 |
completed | May 11, 2026, 10:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01b17486c08190aa47953e74c62e84 |
completed | May 11, 2026, 10:37 a.m. |
Created at: April 10, 2026, 5:46 a.m.