Triple
T8093928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apache Flink |
E188935
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Apache Kafka |
E358076
|
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: Apache Kafka | Statement: [Apache Flink, integratesWith, Apache Kafka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Apache Kafka Context triple: [Apache Flink, integratesWith, Apache Kafka]
-
A.
Apache Kafka
chosen
Apache Kafka is a distributed event streaming platform widely used for building real-time data pipelines and streaming applications.
-
B.
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.
-
C.
Kafka Streams
Kafka Streams is a Java library for building real-time, distributed stream processing applications on top of Apache Kafka.
-
D.
Apache Storm
Apache Storm is a distributed real-time computation system designed for processing large streams of data with low latency and high fault tolerance.
-
E.
Apache Flink
Apache Flink is an open-source distributed stream-processing framework designed for high-throughput, low-latency data processing and real-time analytics on large-scale data.
- 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_69ca82b7b3e88190b9041ab0ef28b3cb |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb429089cc81909e4625f9cc7e305f |
completed | March 31, 2026, 3:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbe81763881909e2e67bb56f2aa83 |
completed | April 1, 2026, 6:43 a.m. |
Created at: March 30, 2026, 5:30 p.m.