Triple
T13681326
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Makki surah |
E328009
|
entity |
| Predicate | includesLiteraryFeature |
P16928
|
FINISHED |
| Object | frequent oaths (qasam) |
—
|
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: frequent oaths (qasam) | Statement: [Makki surah, includesLiteraryFeature, frequent oaths (qasam)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesLiteraryFeature Context triple: [Makki surah, includesLiteraryFeature, frequent oaths (qasam)]
-
A.
literaryFeature
chosen
Indicates a relationship where something possesses or exhibits a characteristic, device, or stylistic element used in literature.
-
B.
literarySubject
Indicates that one entity serves as the subject, topic, or focus of a literary work created by another entity.
-
C.
literaryScript
Indicates a relationship where an entity serves as the written text or script of a literary work, such as a play, film, or other narrative production.
-
D.
literaryThemeInvolvement
Indicates the involvement or presence of a particular literary theme within a work, passage, or character arc.
-
E.
inLiterature
Indicates that a work, concept, or entity is mentioned, discussed, or represented within a piece of literature.
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc66cbb088190907cb89dda8e4ebd |
completed | April 12, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69dbbe8d8d0881908d6e89954f44eed4 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:53 p.m.