Triple
T17885070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joe Casey |
E447178
|
entity |
| Predicate | coCreatorOf |
P806
|
FINISHED |
| Object | Automatic 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: Automatic Kafka | Statement: [Joe Casey, coCreatorOf, Automatic Kafka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Automatic Kafka Context triple: [Joe Casey, coCreatorOf, Automatic Kafka]
-
A.
Automatic Kafka
chosen
Automatic Kafka is a surreal, postmodern superhero comic series created by writer Joe Casey that deconstructs comic-book tropes through its bizarre, self-aware narrative.
-
B.
Apache Kafka
Apache Kafka is a distributed event streaming platform widely used for building real-time data pipelines and streaming applications.
-
C.
Kafka Streams
Kafka Streams is a Java library for building real-time, distributed stream processing applications on top of Apache Kafka.
-
D.
Apache Samza
Apache Samza is a distributed stream processing framework designed for scalable, fault-tolerant processing of real-time data streams, often used with Apache Kafka and YARN.
-
E.
Amazon Kinesis Client Library
Amazon Kinesis Client Library is a software library that simplifies building and scaling applications that process and consume streaming data from Amazon Kinesis data streams.
- 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_69d8b9f59bd48190a6fc925a855b8bac |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49c123748819091bf46178105079b |
completed | April 19, 2026, 9:10 a.m. |
Created at: April 10, 2026, 10:18 a.m.