Triple
T7467133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mahaprithibi |
E176399
|
entity |
| Predicate | hasImageryType |
P77285
|
FINISHED |
| Object | nature 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: nature imagery | Statement: [Mahaprithibi, hasImageryType, nature imagery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasImageryType Context triple: [Mahaprithibi, hasImageryType, nature imagery]
-
A.
hasImagingType
Indicates the specific imaging modality or technique associated with or used in a given imaging procedure or result.
-
B.
hasImageType
Indicates that an entity is associated with an image of a particular type or format.
-
C.
hasLandscapeType
Indicates that an entity possesses or is characterized by a particular type or category of landscape.
-
D.
containsImage
Indicates that one entity includes or embeds an image as part of its content or structure.
-
E.
usesImagery
Indicates that one entity employs descriptive or figurative language to create sensory or vivid mental images in relation to another entity or concept.
- F. None of above. chosen
Provenance (4 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_69c69f223fd88190b4c69b95d7cbeeda |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f3f589cc81909f25268838c7c964 |
completed | March 27, 2026, 9:17 p.m. |
| PD | Predicate disambiguation | batch_69c6f03bad9c8190bdd5abb86d37df47 |
completed | March 27, 2026, 9:01 p.m. |
| PDg | Predicate description generation | batch_69c6f386daac81908ded91b397b44148 |
completed | March 27, 2026, 9:15 p.m. |
Created at: March 27, 2026, 3:40 p.m.