Triple
T1392229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Satanic Verses |
E29984
|
entity |
| Predicate | hasNarrativeStructure |
P22618
|
FINISHED |
| Object | multiple interwoven storylines |
—
|
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: multiple interwoven storylines | Statement: [The Satanic Verses, hasNarrativeStructure, multiple interwoven storylines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNarrativeStructure Context triple: [The Satanic Verses, hasNarrativeStructure, multiple interwoven storylines]
-
A.
hasNarrative
Indicates that one entity contains, presents, or is associated with a story or narrative about another entity or subject.
-
B.
containsNarrativeOf
Indicates that one entity includes or presents the story, account, or narrative content of another entity.
-
C.
hasNarrativeRole
Indicates that an entity participates in a narrative with a specific functional role (e.g., protagonist, antagonist, narrator) relative to the story.
-
D.
hasNarrativeDevice
Indicates that one entity employs, contains, or is characterized by a particular narrative device used in storytelling or discourse.
-
E.
textualStructure
chosen
Indicates how parts of a text are organized and related to each other within its overall structure.
- 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_69a498dc92f8819094a1108f8ac90f43 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c360a7f08190ab7e903764b06fdf |
completed | March 1, 2026, 10:53 p.m. |
| PD | Predicate disambiguation | batch_69a4beffcf808190ab4cd0271257ce63 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:59 p.m.