Triple
T16252028
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hans Kieslowski |
E394531
|
entity |
| Predicate | hasMetafictionalContext |
P12417
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Hans Kieslowski, hasMetafictionalContext, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMetafictionalContext Context triple: [Hans Kieslowski, hasMetafictionalContext, true]
-
A.
hasMetafictionalRole
chosen
Indicates that an entity plays a role within a story that self-consciously comments on, references, or breaks the conventions of fiction itself.
-
B.
hasFictionalContent
Indicates that something contains or includes material that is imaginary, invented, or not intended to represent real events or facts.
-
C.
hasMetaphoricalContent
Indicates that something contains or expresses meaning through metaphorical, rather than purely literal, content.
-
D.
hasFictionalScope
Indicates that something pertains to, applies within, or is limited to a fictional or imagined context rather than real-world scope.
-
E.
hasLiteraryContext
Indicates that something is associated with, situated within, or explained by a particular literary context (such as a work, genre, period, or interpretive framework).
- 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24597b74481908fdb8175628a57a1 |
completed | April 17, 2026, 2:37 p.m. |
| PD | Predicate disambiguation | batch_69e219ee6f6481909663b388dc99770a |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:04 a.m.