Triple
T41105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The American Soldier |
E810
|
entity |
| Predicate | depictedIn |
P626
|
FINISHED |
| Object | American war memorials |
—
|
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: American war memorials | Statement: [The American Soldier, depictedIn, American war memorials]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictedIn Context triple: [The American Soldier, depictedIn, American war memorials]
-
A.
appearsIn
Indicates that an entity is present, featured, or occurs within a particular context, work, or medium.
-
B.
depicts
Indicates that one entity visually represents, portrays, or shows another entity.
-
C.
notableDepictionYear
Indicates the year in which a notable or significant depiction of an entity occurred or was created.
-
D.
featuredIn
chosen
Indicates that one entity appears or is prominently included within another entity, such as a person, work, or item being showcased in a larger work, event, or context.
-
E.
depictsPerson
Indicates that one entity visually represents or portrays a specific person.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24db9527c8190816b6b25c88cb2f4 |
completed | Feb. 28, 2026, 2:06 a.m. |
| PD | Predicate disambiguation | batch_69a24ab8a8908190beec6da6694dd4c9 |
completed | Feb. 28, 2026, 1:54 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.