Triple
T28667920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unaussprechlichen Kulten |
E725630
|
entity |
| Predicate | typicalFormatInFiction |
P180682
|
FINISHED |
| Object | aged leather-bound volume |
—
|
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: aged leather-bound volume | Statement: [Unaussprechlichen Kulten, typicalFormatInFiction, aged leather-bound volume]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalFormatInFiction Context triple: [Unaussprechlichen Kulten, typicalFormatInFiction, aged leather-bound volume]
-
A.
formatInFiction
chosen
Indicates how something is stylistically or structurally presented within a fictional work.
-
B.
hasFormatInFiction
Indicates that a work of fiction is presented or realized in a particular narrative or media format (e.g., novel, film, comic).
-
C.
typicalStyleInFiction
Indicates the characteristic narrative or artistic style that an entity most commonly exhibits within fictional works.
-
D.
typicalLiteraryForms
Indicates the usual or characteristic literary forms or genres associated with a given subject or creator.
-
E.
fictionalType
Indicates that one entity is a fictional or imaginary type or category of the other 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_69f01d85be388190b669a0e401e2f2c4 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_6a00d508290081909f3d5dfbb2e80c8e |
completed | May 10, 2026, 6:57 p.m. |
| PD | Predicate disambiguation | batch_6a00d49da4cc81909566ad286ec22292 |
completed | May 10, 2026, 6:55 p.m. |
Created at: April 28, 2026, 5:02 a.m.