Triple
T27476779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 三木武夫 |
E693484
|
entity |
| Predicate | 出生地 |
P1
|
FINISHED |
| Object |
日本・香川県木田郡平井村(現・さぬき市)
日本・香川県木田郡平井村(現・さぬき市)は、四国北東部に位置し、後に内閣総理大臣となる三木武夫を輩出した香川県の農村地域である。
|
E1775217
|
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: 日本・香川県木田郡平井村(現・さぬき市) | Statement: [三木武夫, 出生地, 日本・香川県木田郡平井村(現・さぬき市)]
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: 日本・香川県木田郡平井村(現・さぬき市) Triple: [三木武夫, 出生地, 日本・香川県木田郡平井村(現・さぬき市)]
Generated description
日本・香川県木田郡平井村(現・さぬき市)は、四国北東部に位置し、後に内閣総理大臣となる三木武夫を輩出した香川県の農村地域である。
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_69ef5381f2648190a2392d0fab833095 |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f62e451dd08190b9cbe3a9a2a4ffa6 |
completed | May 2, 2026, 5:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12bbe717ec8190bc12eee81e3851f2 |
completed | May 24, 2026, 8:50 a.m. |
| NEDg | Description generation | batch_6a12bce144b481909ef46950ddf8236a |
completed | May 24, 2026, 8:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a12bd771268819080f52425e926c3bc |
completed | May 24, 2026, 8:57 a.m. |
Created at: April 27, 2026, 12:57 p.m.