Triple
T31399746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kafir (religious term) |
E800963
|
entity |
| Predicate | hasInterpretationVariation |
P20254
|
FINISHED |
| Object | differs among Islamic scholars |
—
|
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: differs among Islamic scholars | Statement: [Kafir (religious term), hasInterpretationVariation, differs among Islamic scholars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInterpretationVariation Context triple: [Kafir (religious term), hasInterpretationVariation, differs among Islamic scholars]
-
A.
containsInterpretationOf
Indicates that one entity includes or embodies an interpretation or understanding of another entity.
-
B.
hasInterpretationStyle
Indicates a relationship where an entity is associated with a particular manner, method, or style in which it is interpreted or understood.
-
C.
isInterpretedDifferentlyIn
chosen
Indicates that the same item, event, or expression is understood or construed in a different way within a specified context, group, or setting.
-
D.
hasExplicitInterpretation
Indicates that something is associated with a clearly defined and unambiguous meaning or interpretation.
-
E.
isInterpretableIn
Indicates that one formal system, language, or theory can be meaningfully represented, understood, or given a semantics within another system, language, or theory.
- 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_69f224ea9998819086ae2e4f4f4091c8 |
completed | April 29, 2026, 3:34 p.m. |
| NER | Named-entity recognition | batch_69ff2fbae9b48190847eefa1c227d43e |
completed | May 9, 2026, 12:59 p.m. |
| PD | Predicate disambiguation | batch_69ff2f2218048190a32224a648182b5d |
completed | May 9, 2026, 12:57 p.m. |
Created at: April 29, 2026, 9:19 p.m.