Triple
T34371742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 10 to Midnight |
E882173
|
entity |
| Predicate | featuresKillerType |
P204099
|
FINISHED |
| Object | serial killer |
—
|
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: serial killer | Statement: [10 to Midnight, featuresKillerType, serial killer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresKillerType Context triple: [10 to Midnight, featuresKillerType, serial killer]
-
A.
featuresAssassins
Indicates that the subject includes, portrays, or prominently involves assassins as part of its content or composition.
-
B.
featuresIn
Indicates that an entity appears or plays a role within another entity, such as a person or element being included in a work, event, or context.
-
C.
featuresSuit
Indicates that one entity includes or presents a particular suit (e.g., clothing, armor, or outfit) as a notable component or attribute.
-
D.
featuresRiseOf
Indicates that something includes or portrays the emergence or increasing prominence of someone or something.
-
E.
featuresDemon
Indicates that an entity includes, depicts, or prominently involves a demon.
- 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_69f349bf5d7481908dd5da4cbdf74047 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a03232efd8481908ee25447a29ee20b |
completed | May 12, 2026, 12:55 p.m. |
| PD | Predicate disambiguation | batch_6a032269dcb08190907c965c28145b77 |
completed | May 12, 2026, 12:51 p.m. |
| PDg | Predicate description generation | batch_6a03232e53788190abf17396e8d02dbf |
completed | May 12, 2026, 12:55 p.m. |
Created at: May 1, 2026, 1:59 a.m.