Triple
T26420681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Landscape with Footbridge |
E664228
|
entity |
| Predicate | subjectEmphasis |
P53888
|
FINISHED |
| Object | natural environment over human activity |
—
|
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: natural environment over human activity | Statement: [Landscape with Footbridge, subjectEmphasis, natural environment over human activity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectEmphasis Context triple: [Landscape with Footbridge, subjectEmphasis, natural environment over human activity]
-
A.
categoryFocus
chosen
Indicates that one entity is the primary subject, theme, or focal point within the broader category defined by the other entity.
-
B.
subjectMatter
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
C.
subjectImpliedAs
Indicates that the subject of an action or statement is not explicitly stated but is understood or inferred from context.
-
D.
primaryTopicOf
Indicates that a given subject is the main or central topic described by another resource (such as a document, page, or record).
-
E.
subjectKey
Indicates that the subject serves as a unique key or identifier used to reference or distinguish an entity in a relationship or dataset.
- 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_69ee883a04ec81908883c4559f8c7e24 |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f661b58ac48190907b6c6e9ccc2c59 |
completed | May 2, 2026, 8:42 p.m. |
| PD | Predicate disambiguation | batch_69f660eea4648190b0d5e24293607813 |
completed | May 2, 2026, 8:39 p.m. |
Created at: April 26, 2026, 11:43 p.m.