Triple
T716677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Ride for Liberty – The Fugitive Slaves |
E14329
|
entity |
| Predicate | subjectMatterCategory |
P450
|
FINISHED |
| Object | war and conflict in art |
—
|
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: war and conflict in art | Statement: [A Ride for Liberty – The Fugitive Slaves, subjectMatterCategory, war and conflict in art]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectMatterCategory Context triple: [A Ride for Liberty – The Fugitive Slaves, subjectMatterCategory, war and conflict in art]
-
A.
subjectMatter
chosen
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
B.
category
Indicates that one entity is classified as a member or type within the grouping or class defined by another entity.
-
C.
subjectOfWork
Indicates that one entity is the main topic, focus, or theme that a particular work (such as a book, article, or artwork) is about.
-
D.
branchOfLaw
Indicates a relationship where one legal field or discipline is a subdivision or specialized area within a broader body of law.
-
E.
legalSubject
Indicates that an entity is the bearer of legal rights, duties, or responsibilities within a legal relationship or context.
- 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_69a4934a36e081909e7abef98b898a4e |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a57649dc8190bfdee2f9c0c90415 |
completed | March 1, 2026, 8:45 p.m. |
| PD | Predicate disambiguation | batch_69a4a4f38898819089d79bad4f4ff2d2 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.