Triple
T25093368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Islam (in fictionalized form) |
E628523
|
entity |
| Predicate | treatmentOfFigures |
P158774
|
FINISHED |
| Object | prophetic figures rendered as fictional characters |
—
|
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: prophetic figures rendered as fictional characters | Statement: [Islam (in fictionalized form), treatmentOfFigures, prophetic figures rendered as fictional characters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: treatmentOfFigures Context triple: [Islam (in fictionalized form), treatmentOfFigures, prophetic figures rendered as fictional characters]
-
A.
numberOfFiguresDepicted
Indicates the total count of distinct figures shown within a given depiction or representation.
-
B.
containsHumanFigures
Indicates that the subject includes one or more human figures within its content or composition.
-
C.
featuresFigureOf
Indicates that one entity includes or presents another entity as a figure, illustration, or visual element.
-
D.
typicalFigure
Indicates that one entity serves as a standard or representative example (a typical instance) of the other entity.
-
E.
trainedFigure
Indicates that one entity has been trained, coached, or otherwise prepared by another entity.
- 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_69e2ff2f58e881908340527bc5d34f07 |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f48b9b687881908fd87a2f5fa0b1e7 |
completed | May 1, 2026, 11:16 a.m. |
| PD | Predicate disambiguation | batch_69f48060597c8190a4414e4e4fcb1fec |
completed | May 1, 2026, 10:28 a.m. |
| PDg | Predicate description generation | batch_69f48b9058d081908ec9af261ee092e2 |
completed | May 1, 2026, 11:16 a.m. |
Created at: April 18, 2026, 6:24 a.m.