Triple
T19684131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phyllis Dietrichson |
E472667
|
entity |
| Predicate | genreArchetype |
P102015
|
FINISHED |
| Object | classic film noir femme fatale |
—
|
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: classic film noir femme fatale | Statement: [Phyllis Dietrichson, genreArchetype, classic film noir femme fatale]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genreArchetype Context triple: [Phyllis Dietrichson, genreArchetype, classic film noir femme fatale]
-
A.
genreRole
Indicates a relationship where an entity holds a specific functional or categorical role within a particular genre.
-
B.
usesCharacterArchetype
chosen
Indicates that one entity employs or incorporates the character archetype represented by another entity in its narrative or design.
-
C.
genreOfCharacter
Indicates that a character belongs to or is associated with a particular genre (such as fantasy, horror, or comedy).
-
D.
genre
Indicates the artistic or thematic category to which a work (such as a book, film, or song) belongs.
-
E.
genreFunction
Indicates the role or purpose that a genre serves in relation to an entity, such as how it functions within classification, interpretation, or use.
- 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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e641c225fc81909637d304891f4abd |
completed | April 20, 2026, 3:09 p.m. |
| PD | Predicate disambiguation | batch_69e53039ea808190a9106a53f564ab92 |
completed | April 19, 2026, 7:42 p.m. |
Created at: April 10, 2026, 1:45 p.m.