Triple
T15063786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cameron’s Closet |
E379702
|
entity |
| Predicate | hasDemonCharacter |
P117174
|
FINISHED |
| Object | demonic entity from another dimension |
—
|
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: demonic entity from another dimension | Statement: [Cameron’s Closet, hasDemonCharacter, demonic entity from another dimension]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDemonCharacter Context triple: [Cameron’s Closet, hasDemonCharacter, demonic entity from another dimension]
-
A.
hasVampireCharacter
Indicates that an entity includes or features at least one character who is a vampire.
-
B.
hasGhostCharacter
Indicates that an entity includes, features, or is associated with a character that is a ghost.
-
C.
hasThiefCharacter
Indicates that an entity includes or features a character whose role or identity is that of a thief.
-
D.
hasMermaidCharacter
Indicates that an entity includes, features, or is associated with a character who is a mermaid.
-
E.
hasPatronCharacter
Indicates that one entity serves as a patron, protector, or guiding figure for another entity.
- F. None of above. chosen
Provenance (4 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_69d85cd7683881908d405c1b5d7b4f7f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69dedee803ac81908bb7d66e49c2eb72 |
completed | April 15, 2026, 12:42 a.m. |
| PD | Predicate disambiguation | batch_69deb95a182081908fffc4402b02a394 |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec71e8dcc81908badc834b6ccf273 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:02 a.m.