Triple
T3285292
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Captain Vidal |
E68964
|
entity |
| Predicate | alignmentInFiction |
P16437
|
FINISHED |
| Object | villainous |
—
|
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: villainous | Statement: [Captain Vidal, alignmentInFiction, villainous]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alignmentInFiction Context triple: [Captain Vidal, alignmentInFiction, villainous]
-
A.
alignedAgainst
Indicates that two or more entities are united in opposition to a common target, side, or objective.
-
B.
alignmentConcept
chosen
Indicates a conceptual or abstract relationship of alignment or correspondence between entities, such as agreement, compatibility, or shared orientation in some dimension.
-
C.
fictionalMedium
Indicates that a work of fiction is presented or conveyed through a particular medium or format (such as a book, film, game, or comic).
-
D.
inLiterature
Indicates that a work, concept, or entity is mentioned, discussed, or represented within a piece of literature.
-
E.
hasFictionComponent
Indicates that something includes, contains, or is composed in part of a fictional element or work.
- 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_69ad859c463481909ca4be267336c290 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb03918c48190987d7cfd3bda9716 |
completed | March 8, 2026, 5:22 p.m. |
| PD | Predicate disambiguation | batch_69ada421fadc8190b7c7d3c8afd20061 |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:10 p.m.