Triple
T29582102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jack Torrance about the hotel’s history |
E753615
|
entity |
| Predicate | setsTone |
P49759
|
FINISHED |
| Object | ominous |
—
|
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: ominous | Statement: [Jack Torrance about the hotel’s history, setsTone, ominous]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: setsTone Context triple: [Jack Torrance about the hotel’s history, setsTone, ominous]
-
A.
hasToneFunction
Indicates that one entity serves a specific tonal or harmonic function in relation to another entity within a musical context.
-
B.
supportsTone
Indicates that one entity is compatible with, enables, or can correctly handle a specified tone or tonal characteristic of another entity.
-
C.
setToMusicAs
Indicates that one entity (typically a text or work) has been adapted and arranged by another entity into a musical composition.
-
D.
commonlySetToTune
Indicates that something is frequently assigned or adjusted to a particular tune or musical setting.
-
E.
contributesToTone
chosen
Indicates that one entity plays a role in shaping, influencing, or determining the overall tone or mood of 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_69f0ef80bf8c8190ad286e99f7df0c63 |
completed | April 28, 2026, 5:33 p.m. |
| NER | Named-entity recognition | batch_69f66d7a4460819092b22b56609cc7ed |
completed | May 2, 2026, 9:32 p.m. |
| PD | Predicate disambiguation | batch_69f6659d36208190b01412600a4ed57d |
completed | May 2, 2026, 8:59 p.m. |
Created at: April 28, 2026, 6:07 p.m.