Triple
T13583033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quran 86:1–17 |
E324470
|
entity |
| Predicate | endsWithVerse |
P5466
|
FINISHED |
| Object | Quran 86:17 |
—
|
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: Quran 86:17 | Statement: [Quran 86:1–17, endsWithVerse, Quran 86:17]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: endsWithVerse Context triple: [Quran 86:1–17, endsWithVerse, Quran 86:17]
-
A.
containsVerse
Indicates that one entity (typically a text or collection) includes a specific verse as part of its content.
-
B.
closingVerse
chosen
Indicates that one entity serves as the final or concluding verse of another entity, such as a song, poem, or hymn.
-
C.
hasVerseRange
Indicates a relationship where a text or passage is associated with a specific contiguous range of verses it spans.
-
D.
hasVerseCount
Indicates that an entity (such as a text or section) is associated with a specific number of verses it contains.
-
E.
hasVerseStyle
Indicates that one entity is characterized by, or presented in, the verse style specified by another entity.
- 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_69d80769100c819099111274614f5ed2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb031e8048190a5f2ea934308036c |
completed | April 12, 2026, 2:46 p.m. |
| PD | Predicate disambiguation | batch_69dbae161a0481909f9d3f40ca4e0ac5 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:49 p.m.