Triple
T34500547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Billy the Kid Versus Dracula |
E885736
|
entity |
| Predicate | hasVampireAntagonist |
P57422
|
FINISHED |
| Object | Count Dracula |
—
|
NE NERFINISHED |
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: Count Dracula | Statement: [Billy the Kid Versus Dracula, hasVampireAntagonist, Count Dracula]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVampireAntagonist Context triple: [Billy the Kid Versus Dracula, hasVampireAntagonist, Count Dracula]
-
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.
hasVillain
Indicates that one entity is the villain or primary antagonist associated with another entity.
-
D.
hasSharkAntagonist
Indicates that an entity features a shark serving as an opposing or hostile force, often in a central conflict role.
-
E.
hasAntagonisticProtagonist
Indicates that the work features a main character who opposes or undermines the typical heroic or moral expectations of a traditional protagonist.
- 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_69f349cc0220819081f154c6964f4dc2 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71fb1ab3881908e2f7c0e6f23db49 |
completed | May 3, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69f71cc6397881909aaad37a9daa8a7e |
completed | May 3, 2026, 10 a.m. |
Created at: May 1, 2026, 2:01 a.m.