Triple
T18350966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franz Kafka short fiction corpus |
E439665
|
entity |
| Predicate | hasPrimarySettingType |
P3538
|
FINISHED |
| Object | bureaucratic institutions |
—
|
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: bureaucratic institutions | Statement: [Franz Kafka short fiction corpus, hasPrimarySettingType, bureaucratic institutions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrimarySettingType Context triple: [Franz Kafka short fiction corpus, hasPrimarySettingType, bureaucratic institutions]
-
A.
hasSetting
chosen
Indicates that an entity takes place, occurs, or exists within a particular environment, context, or location.
-
B.
hasPrimaryFeature
Indicates that an entity possesses a main or most characteristic feature that defines or distinguishes it.
-
C.
hasPrimaryAccessType
Indicates that an entity is associated with its main or default mode or category of access.
-
D.
hasPrimaryTrafficType
Indicates that an entity is associated with a main or predominant type of traffic it handles or is designed for.
-
E.
hasPrimary
Indicates that one entity is designated as the main or most important instance (the primary) in relation to another entity.
- 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_69d8b918221c8190a9f7b563d64ac677 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e514f83b648190b473cf611851c666 |
completed | April 19, 2026, 5:46 p.m. |
| PD | Predicate disambiguation | batch_69e44fe91bc08190906518e1b120fcf0 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:37 a.m.