Triple
T18879331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agnes Bruckner |
E461778
|
entity |
| Predicate | castInGenre |
P133307
|
FINISHED |
| Object | teen horror films |
—
|
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: teen horror films | Statement: [Agnes Bruckner, castInGenre, teen horror films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: castInGenre Context triple: [Agnes Bruckner, castInGenre, teen horror films]
-
A.
castIn
Indicates that an actor or performer appears in a particular film, show, or production.
-
B.
castType
Indicates the specific kind or category of casting relationship that exists between two entities.
-
C.
castingChoiceFor
Indicates a relationship where a particular casting decision or option is selected or designated for a specific role, production, or performance.
-
D.
castFeature
Indicates that a particular actor or performer is part of the cast for a given feature (such as a film, show, or other production).
-
E.
castInYear
Indicates that an entity was cast in a role or production that took place in a specified year.
- 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_69d8dcfc3430819095ee6fc0eb4c06a5 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c3d06ef481908bba297d7a1fd011 |
completed | April 20, 2026, 6:12 a.m. |
| PD | Predicate disambiguation | batch_69e48d22dde8819093b1d963bd673365 |
completed | April 19, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e4978813d081908eaf25bb727fc6cf |
completed | April 19, 2026, 8:51 a.m. |
Created at: April 10, 2026, 11:57 a.m.