Triple
T19896460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guler |
E478163
|
entity |
| Predicate | religiousArtThemes |
P24743
|
FINISHED |
| Object | Krishna legends |
—
|
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: Krishna legends | Statement: [Guler, religiousArtThemes, Krishna legends]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: religiousArtThemes Context triple: [Guler, religiousArtThemes, Krishna legends]
-
A.
hasReligiousTheme
chosen
Indicates that something (such as a work, event, or object) centrally involves or expresses religious ideas, symbols, practices, or narratives.
-
B.
iconographicSubject
Indicates that one entity serves as the depicted subject or theme represented in the iconography of another entity.
-
C.
ritualTheme
Indicates that one entity has a ritual as a central subject, motif, or focus in relation to the other entity.
-
D.
iconographicCategory
Indicates the classification of an entity based on the type or theme of its visual or symbolic representation.
-
E.
artisticTheme
Indicates the central artistic subject, concept, or motif that characterizes or is expressed by a creative work.
- 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_69d8e520682081909892916424699bd5 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6593dba78819082c8b80e65246171 |
completed | April 20, 2026, 4:50 p.m. |
| PD | Predicate disambiguation | batch_69e537ecda248190895c96afb6243823 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:52 p.m.