Triple
T8545447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nocturnal Turnings or How Siamese Twins Have Sex |
E202311
|
entity |
| Predicate | literaryThemes |
P61759
|
FINISHED |
| Object | desire |
—
|
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: desire | Statement: [Nocturnal Turnings or How Siamese Twins Have Sex, literaryThemes, desire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literaryThemes Context triple: [Nocturnal Turnings or How Siamese Twins Have Sex, literaryThemes, desire]
-
A.
literaryThemeInvolvement
chosen
Indicates the involvement or presence of a particular literary theme within a work, passage, or character arc.
-
B.
literarySubject
Indicates that one entity serves as the subject, topic, or focus of a literary work created by another entity.
-
C.
literaryInterest
Indicates that one entity has an interest in, appreciation of, or engagement with the literary works or writings of another entity.
-
D.
inLiterature
Indicates that a work, concept, or entity is mentioned, discussed, or represented within a piece of literature.
-
E.
literaryFeature
Indicates a relationship where something possesses or exhibits a characteristic, device, or stylistic element used in 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_69ca832461e88190a654c5e44e233aa8 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe74f62148190bfd6cacf5d4f74b6 |
completed | March 31, 2026, 3:25 p.m. |
| PD | Predicate disambiguation | batch_69cbd113e05c81908f4f3fc1b5925164 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:18 p.m.