Triple
T19169508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taste the Blood of Dracula |
E469278
|
entity |
| Predicate | hasVampireTheme |
P57422
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Taste the Blood of Dracula, hasVampireTheme, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVampireTheme Context triple: [Taste the Blood of Dracula, hasVampireTheme, true]
-
A.
hasVampireCharacter
chosen
Indicates that an entity includes or features at least one character who is a vampire.
-
B.
hasVampireMaker
Indicates that one entity is the creator or sire who turned another entity into a vampire.
-
C.
hasDoppelgangerTheme
Indicates that something features or involves a doppelganger-related theme, such as doubles, look-alikes, or mirrored identities.
-
D.
hasMuseumTheme
Indicates that something (such as a museum, exhibit, or collection) is characterized by or dedicated to a particular thematic focus.
-
E.
hasAttractionTheme
Indicates that something (such as a place, event, or attraction) is characterized by or associated with a particular theme or motif.
- 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_69d8dd09d5a081909ae43c286651ae5a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f162ac648190a5f60c6a77b68304 |
completed | April 20, 2026, 9:26 a.m. |
| PD | Predicate disambiguation | batch_69e4b9b83d6881908e6271c620f74100 |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 12:06 p.m.