Triple
T27436626
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenji Doihara |
E690802
|
entity |
| Predicate | nativeName |
P15
|
FINISHED |
| Object |
土肥原 賢二
土肥原 賢二(Kenji Doihara)は、第二次世界大戦期の日本陸軍軍人であり、満州事変の画策や諜報活動で知られ、戦後A級戦犯として処刑された人物である。
|
E1773752
|
NE FINISHED |
How this triple was built (2 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: 土肥原 賢二 | Statement: [Kenji Doihara, nativeName, 土肥原 賢二]
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: 土肥原 賢二 Triple: [Kenji Doihara, nativeName, 土肥原 賢二]
Generated description
土肥原 賢二(Kenji Doihara)は、第二次世界大戦期の日本陸軍軍人であり、満州事変の画策や諜報活動で知られ、戦後A級戦犯として処刑された人物である。
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_69ef5200fa0481908e28508d6e2c149e |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f62d89b89c8190afb372a8172111e7 |
completed | May 2, 2026, 4:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12b252b7088190b4c187a3fae4ba20 |
completed | May 24, 2026, 8:09 a.m. |
| NEDg | Description generation | batch_6a12b43a4b008190917ce4b7f25e670b |
completed | May 24, 2026, 8:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a12b50444b88190947f0c2989954233 |
completed | May 24, 2026, 8:21 a.m. |
Created at: April 27, 2026, 12:44 p.m.