Triple
T21386159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ジョー・ローゼンタール |
E527502
|
entity |
| Predicate | 軍歴・立場 |
P38925
|
FINISHED |
| Object | 民間人カメラマンとして米軍に同行 |
—
|
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: 民間人カメラマンとして米軍に同行 | Statement: [ジョー・ローゼンタール, 軍歴・立場, 民間人カメラマンとして米軍に同行]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 軍歴・立場 Context triple: [ジョー・ローゼンタール, 軍歴・立場, 民間人カメラマンとして米軍に同行]
-
A.
formerMilitaryRole
Indicates that an entity previously held, but no longer holds, a specific military role or position.
-
B.
militaryBackground
chosen
Indicates that an entity has prior or current experience, service, or training in a military organization.
-
C.
militaryRole
Indicates the specific function, position, or duty an entity holds within a military organization or context.
-
D.
militaryStatus
Indicates the relationship between an entity and a military organization in terms of service condition, such as active duty, reserve, veteran, or non-military status.
-
E.
hadMilitaryPost
Indicates that an entity held an official position or assignment within a military organization.
- 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.