Triple
T7310143
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Islamic Republic |
E168068
|
entity |
| Predicate | canVaryBy |
P58820
|
FINISHED |
| Object | school of Islamic jurisprudence |
—
|
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: school of Islamic jurisprudence | Statement: [Islamic Republic, canVaryBy, school of Islamic jurisprudence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canVaryBy Context triple: [Islamic Republic, canVaryBy, school of Islamic jurisprudence]
-
A.
hasVariability
Indicates that an entity exhibits variation or fluctuation in its state, value, or characteristics over time or across instances.
-
B.
hasVariance
Indicates that there is a measurable degree of variability or dispersion in the values or outcomes associated with the related entities.
-
C.
compositionVariesBy
chosen
Indicates that the composition of something differs depending on a specified factor, condition, or context.
-
D.
usageVariesBy
Indicates that the way something is used differs depending on a specified factor, such as context, user, location, or conditions.
-
E.
hasVariabilityType
Indicates that an entity is associated with a specific kind or category of variability (e.g., how or in what way it varies).
- 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_69c6888d8e3c81909db79714903baf31 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6ebdbd6f481908e69e53dab452656 |
completed | March 27, 2026, 8:43 p.m. |
| PD | Predicate disambiguation | batch_69c6e7705f4881909793071dee50c557 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 3:01 p.m.