Triple
T18633404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ottilie Kafka |
E455478
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Kafka |
—
|
NE NERFINISHED |
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: Kafka | Statement: [Ottilie Kafka, familyName, Kafka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kafka Context triple: [Ottilie Kafka, familyName, Kafka]
-
A.
Kafka
chosen
Kafka is a distributed event streaming platform widely used for building real-time data pipelines and messaging systems.
-
B.
Kafka
Kafka is a 1991 mystery thriller film directed by Steven Soderbergh that blends biographical elements of writer Franz Kafka’s life with surreal, Kafkaesque fiction.
-
C.
Kafka’s Dick
Kafka’s Dick is a satirical stage play by Alan Bennett that imagines Franz Kafka and his executor Max Brod confronting their posthumous reputations in modern-day England.
-
D.
Kafka y sus precursores
Kafka y sus precursores es un célebre ensayo de Jorge Luis Borges en el que analiza la obra de Franz Kafka a través de sus antecedentes literarios y la idea de que un autor puede crear a sus precursores.
-
E.
John Kafka
John Kafka is an American film director best known for his work on animated features and sequels, including Disney projects such as Cinderella II: Dreams Come True.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d38cc7948190a55ea64e5638994e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54fc5c7ec8190ab0c64f009583f96 |
completed | April 19, 2026, 9:57 p.m. |
Created at: April 10, 2026, 11:46 a.m.