Triple
T14440733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apache Kafka |
E358076
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | Kafka Streams |
E702192
|
NE 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: Kafka Streams | Statement: [Apache Kafka, hasComponent, Kafka Streams]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kafka Streams Context triple: [Apache Kafka, hasComponent, Kafka Streams]
-
A.
Kafka Streams
chosen
Kafka Streams is a Java library for building real-time, distributed stream processing applications on top of Apache Kafka.
-
B.
Apache Kafka
Apache Kafka is a distributed event streaming platform widely used for building real-time data pipelines and streaming applications.
-
C.
Akka Streams
Akka Streams is a library for building and running asynchronous, backpressure-aware data processing pipelines on the JVM, based on the Reactive Streams specification.
-
D.
DataStream API
DataStream API is Apache Flink’s core streaming abstraction for building stateful, event-driven data processing applications over unbounded and bounded data streams.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8279402a88190821ffa39ae15bccf |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de914c1398819090fa2a74d257ba3e |
completed | April 14, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bda6ee88190aeec77092eb3576a |
completed | May 8, 2026, 3:43 a.m. |
Created at: April 10, 2026, 1:18 a.m.