Triple
T20178195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Night of the Living Dummy II |
E492653
|
entity |
| Predicate | hasMotiveForHorror |
P64901
|
FINISHED |
| Object | dummy comes to life and causes mischief and terror |
—
|
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: dummy comes to life and causes mischief and terror | Statement: [Night of the Living Dummy II, hasMotiveForHorror, dummy comes to life and causes mischief and terror]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMotiveForHorror Context triple: [Night of the Living Dummy II, hasMotiveForHorror, dummy comes to life and causes mischief and terror]
-
A.
hasHorrorElements
chosen
Indicates that something contains features, themes, or stylistic aspects characteristic of the horror genre.
-
B.
hasMotiveOfCriminals
Indicates that the specified motive is attributed to or associated with the criminals in question.
-
C.
hasHorrorSubgenre
Indicates that a horror work is classified as belonging to a specific horror subgenre.
-
D.
notableFear
Indicates that one entity is especially afraid of, or has a significant fear related to, another entity.
-
E.
hasMurderer
Indicates that one entity is the person who committed the murder 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_69da6268a034819081cbd9ea5a1c9475 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e668ed07c8819091bd9ffda237a91c |
completed | April 20, 2026, 5:57 p.m. |
| PD | Predicate disambiguation | batch_69e55b0c11cc8190836d1eee5945f000 |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:36 p.m.