Triple
T20385281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AFI's 100 Years...100 Heroes & Villains |
E497942
|
entity |
| Predicate | numberOfVillainsListed |
P139915
|
FINISHED |
| Object | 50 |
—
|
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: 50 | Statement: [AFI's 100 Years...100 Heroes & Villains, numberOfVillainsListed, 50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfVillainsListed Context triple: [AFI's 100 Years...100 Heroes & Villains, numberOfVillainsListed, 50]
-
A.
hasVillain
Indicates that one entity is the villain or primary antagonist associated with another entity.
-
B.
numberOfDemonsDescribed
Indicates the count of demons that are specified or described in relation to a given subject.
-
C.
afis100HeroesVillainsRank
Indicates the ranking position of a character within the AFI’s “100 Heroes & Villains” list.
-
D.
episodeVillainServed
Indicates that a villain appears in an episode in the role of a servant or subordinate to another character or force.
-
E.
introducedIconicVillains
Indicates that an entity is responsible for first presenting or bringing into prominence villains that later became widely recognized as iconic.
- 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_69e0b4a71ebc8190b153a36c738730f4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6790a31a4819099b2e6df2bafe547 |
completed | April 20, 2026, 7:05 p.m. |
| PD | Predicate disambiguation | batch_69e57648be3c81908256838228cabf5c |
completed | April 20, 2026, 12:41 a.m. |
| PDg | Predicate description generation | batch_69e58d7481508190a87c8b88f9df9879 |
completed | April 20, 2026, 2:20 a.m. |
Created at: April 16, 2026, 11:28 a.m.