Triple
T24239257
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ar-Rahim |
E603174
|
entity |
| Predicate | contrastExplanation |
P155308
|
FINISHED |
| Object | Ar-Rahman denotes all-encompassing mercy |
—
|
NE NERFINISHED |
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: Ar-Rahman denotes all-encompassing mercy | Statement: [Ar-Rahim, contrastExplanation, Ar-Rahman denotes all-encompassing mercy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: contrastExplanation Context triple: [Ar-Rahim, contrastExplanation, Ar-Rahman denotes all-encompassing mercy]
-
A.
contrastUse
Indicates that one entity is used in opposition or distinction to another to highlight differences between them.
-
B.
contrastCharacteristic
Indicates that two entities are being compared by highlighting opposing or significantly different characteristics between them.
-
C.
contrastGoal
Indicates a relationship where one goal is defined in opposition to, or as a contrasting alternative to, another goal.
-
D.
exploresContrastBetween
Indicates a relationship in which one entity examines, highlights, or analyzes the differences or oppositions between two or more entities, ideas, or situations.
-
E.
contrastMechanism
Indicates a relationship where one mechanism is highlighted or explained by comparing it against a different, opposing, or alternative mechanism.
- 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_69e2953f631c819097cbb421046bd417 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f28a9eb68c81908a8293c00e581b41 |
completed | April 29, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69f1c448abec8190b87cbf9ed419a309 |
completed | April 29, 2026, 8:41 a.m. |
| PDg | Predicate description generation | batch_69f1c6d4e99081909f61899eccafb73e |
completed | April 29, 2026, 8:52 a.m. |
Created at: April 18, 2026, 12:03 a.m.