Triple
T7985613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apache Storm |
E185674
|
entity |
| Predicate | competesWith |
P1375
|
FINISHED |
| Object |
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.
|
E710969
|
NE FINISHED |
How this triple was built (4 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 Samza | Statement: [Apache Storm, competesWith, Apache Samza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Apache Samza Context triple: [Apache Storm, competesWith, Apache Samza]
-
A.
Apache Kafka
Apache Kafka is a distributed event streaming platform widely used for building real-time data pipelines and streaming applications.
-
B.
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.
-
C.
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.
-
D.
Apache Tez
Apache Tez is a distributed data processing framework designed for building high-performance batch and interactive data workflows on Hadoop.
-
E.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Apache Samza Triple: [Apache Storm, competesWith, Apache Samza]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Apache Samza Target entity description: 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.
-
A.
Apache Kafka
Apache Kafka is a distributed event streaming platform widely used for building real-time data pipelines and streaming applications.
-
B.
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.
-
C.
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.
-
D.
Apache Tez
Apache Tez is a distributed data processing framework designed for building high-performance batch and interactive data workflows on Hadoop.
-
E.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
- F. None of above. chosen
Provenance (5 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_69ca829a2cfc819083d591d58ec04075 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c4a55b881909a96133e56c0dffa |
completed | March 31, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc63b96ed48190b752865ef3855e46 |
completed | April 1, 2026, 12:15 a.m. |
| NEDg | Description generation | batch_69cc64bd6a088190b77e2709c76579e4 |
completed | April 1, 2026, 12:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cc66b0e1548190840e4335ff2b130f |
completed | April 1, 2026, 12:28 a.m. |
Created at: March 30, 2026, 5:15 p.m.