Triple
T29542099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black Rain (1989 film) |
E749529
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Hisa Iino
Hisa Iino is a Japanese film producer best known for her work on the 1989 drama "Black Rain," which depicts the aftermath of the Hiroshima atomic bombing.
|
E2071072
|
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: Hisa Iino | Statement: [Black Rain (1989 film), producer, Hisa Iino]
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: Hisa Iino Triple: [Black Rain (1989 film), producer, Hisa Iino]
Generated description
Hisa Iino is a Japanese film producer best known for her work on the 1989 drama "Black Rain," which depicts the aftermath of the Hiroshima atomic bombing.
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_69f0bd48691081908cecad39bac591e0 |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69f66ccb2f0c8190afec245ff546681c |
completed | May 2, 2026, 9:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3675f1bddc81909cdf39baccf85bfd |
completed | June 20, 2026, 11:13 a.m. |
| NEDg | Description generation | batch_6a367705f3d081909cbb6740ebf20535 |
completed | June 20, 2026, 11:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a36776a62848190ae95f5ba56ea5e43 |
completed | June 20, 2026, 11:20 a.m. |
Created at: April 28, 2026, 5:03 p.m.