Triple
T24728731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Dartmouth College murals |
E618224
|
entity |
| Predicate | languageOfVisualElements |
P128346
|
FINISHED |
| Object | symbolic imagery |
—
|
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: symbolic imagery | Statement: [The Dartmouth College murals, languageOfVisualElements, symbolic imagery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfVisualElements Context triple: [The Dartmouth College murals, languageOfVisualElements, symbolic imagery]
-
A.
languageDisplays
Indicates that one entity presents, shows, or renders another entity in a particular language or linguistic form.
-
B.
languageOfTheme
Indicates that a particular language is used to express, describe, or label a given theme or subject.
-
C.
visualElements
Indicates that one entity contains, uses, or is characterized by specific visual components or graphical features associated with another entity.
-
D.
languageOfTextInImages
Indicates the language used in the textual content that appears within images.
-
E.
languageOfIconography
chosen
Indicates the language used in the symbolic or visual elements (iconography) associated with an entity.
- 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_69e2fab772608190b74163751047ff50 |
completed | April 18, 2026, 3:29 a.m. |
| NER | Named-entity recognition | batch_69f5f6baf2d48190a6a4cd6501be87d2 |
completed | May 2, 2026, 1:06 p.m. |
| PD | Predicate disambiguation | batch_69f5afd5baac8190bb8ed576813c8591 |
completed | May 2, 2026, 8:03 a.m. |
Created at: April 18, 2026, 4:01 a.m.