Triple
T21386150
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ジョー・ローゼンタール |
E527502
|
entity |
| Predicate | 写真のジャンル |
P62927
|
FINISHED |
| Object | 戦争写真 |
—
|
LITERAL 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: 戦争写真 | Statement: [ジョー・ローゼンタール, 写真のジャンル, 戦争写真]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 写真のジャンル Context triple: [ジョー・ローゼンタール, 写真のジャンル, 戦争写真]
-
A.
photographyGenre
chosen
Indicates the specific genre or style of photography that characterizes a photographic work or activity.
-
B.
typeOfStills
Indicates that one entity is a specific kind or category of stills (e.g., a particular type or style within the broader class of still images or still photography).
-
C.
isPhotographicSubject
Indicates that an entity serves as the subject or main focus captured in a photograph taken by another entity.
-
D.
visualGenre
Indicates the visual or stylistic category to which something belongs, such as its artistic or cinematic genre.
-
E.
genreOfRecognition
Indicates the specific genre or category in which an entity (such as a work or person) is formally recognized, honored, or awarded.
- F. None of above.
Provenance (3 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_69e0b51f363c8190944000ab5523b02b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0f3d37c8190b43ec77cdb1904c8 |
completed | April 22, 2026, 11:28 a.m. |
| PD | Predicate disambiguation | batch_69e6162bbfc88190a3e75859941b2638 |
completed | April 20, 2026, 12:03 p.m. |
Created at: April 16, 2026, 5:12 p.m.