Triple
T35831153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macklyn Warlow |
E1035801
|
entity |
| Predicate | majorAntagonistIn |
P67690
|
FINISHED |
| Object | True Blood |
—
|
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: True Blood | Statement: [Macklyn Warlow, majorAntagonistIn, True Blood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: majorAntagonistIn Context triple: [Macklyn Warlow, majorAntagonistIn, True Blood]
-
A.
primaryAntagonistType
Indicates the role or category of the main opposing force or adversary that serves as the central source of conflict.
-
B.
mainAntagonistPortrayedBy
Indicates that the person is the primary actor who plays the main antagonist character in a work.
-
C.
primaryAntagonists
Indicates that the referenced entities serve as the main opposing or adversarial forces in relation to a specified subject or narrative.
-
D.
leadAntagonistCharacter
Indicates that one character serves as the primary opposing or villainous force in relation to another entity in the narrative.
-
E.
isCentralAntagonist
chosen
Indicates that an entity serves as the primary opposing force or main villain driving conflict against the protagonist or central characters.
- 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_69f76e192a94819082db360cb91e6a8d |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff9a25407c81909faa86e72a7a9d17 |
completed | May 9, 2026, 8:33 p.m. |
| PD | Predicate disambiguation | batch_69ff99c613688190a03b2f93d5ccad2b |
completed | May 9, 2026, 8:32 p.m. |
Created at: May 3, 2026, 4:06 p.m.