Triple
T834803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franz Kafka |
E18045
|
entity |
| Predicate | hasCanonicalNameAdjective |
P19207
|
FINISHED |
| Object | Kafkaesque |
—
|
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: Kafkaesque | Statement: [Franz Kafka, hasCanonicalNameAdjective, Kafkaesque]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCanonicalNameAdjective Context triple: [Franz Kafka, hasCanonicalNameAdjective, Kafkaesque]
-
A.
hasAdjectiveEnding
Indicates that something possesses or is marked by a particular adjective-like ending or suffix.
-
B.
hasCanonicalNameForm
chosen
Indicates that an entity is associated with its standard or officially recognized name form.
-
C.
isGivenNameFormOf
Indicates that one name is a given-name variant or form derived from another name.
-
D.
hasMasculineForm
Indicates that an entity has a corresponding masculine grammatical or lexical form.
-
E.
hasGrammaticalGender
Indicates that one entity assigns or possesses a specific grammatical gender in relation to another entity (such as a word, phrase, or linguistic unit).
- 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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abccb94881909cd49aa3fd986b4a |
completed | March 1, 2026, 9:12 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7c7df881909c539c3ab8ff0367 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.