Triple
T8093950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apache Flink |
E188935
|
entity |
| Predicate | hasAPI |
P182
|
FINISHED |
| Object |
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.
|
E711831
|
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: DataStream API | Statement: [Apache Flink, hasAPI, DataStream API]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DataStream API Context triple: [Apache Flink, hasAPI, DataStream API]
-
A.
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.
-
B.
Structured Streaming
Structured Streaming is Apache Spark’s scalable, fault-tolerant stream processing engine that lets developers express streaming computations using the same high-level APIs as batch processing.
-
C.
Kafka Streams
Kafka Streams is a Java library for building real-time, distributed stream processing applications on top of Apache Kafka.
-
D.
Apache Beam
Apache Beam is an open-source unified programming model for defining and executing batch and streaming data processing pipelines across multiple execution engines.
-
E.
IBM Streams
IBM Streams is a high-performance stream processing platform that enables real-time ingestion, analysis, and correlation of large-scale data in motion for enterprise applications.
- 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: DataStream API Triple: [Apache Flink, hasAPI, DataStream API]
Generated description
DataStream API is Apache Flink’s core streaming abstraction for building stateful, event-driven data processing applications over unbounded and bounded data streams.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: DataStream API Target entity description: DataStream API is Apache Flink’s core streaming abstraction for building stateful, event-driven data processing applications over unbounded and bounded data streams.
-
A.
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.
-
B.
Structured Streaming
Structured Streaming is Apache Spark’s scalable, fault-tolerant stream processing engine that lets developers express streaming computations using the same high-level APIs as batch processing.
-
C.
Kafka Streams
Kafka Streams is a Java library for building real-time, distributed stream processing applications on top of Apache Kafka.
-
D.
Apache Beam
Apache Beam is an open-source unified programming model for defining and executing batch and streaming data processing pipelines across multiple execution engines.
-
E.
IBM Streams
IBM Streams is a high-performance stream processing platform that enables real-time ingestion, analysis, and correlation of large-scale data in motion for enterprise applications.
- 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_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_69cc64112138819096050975d707d8ee |
completed | April 1, 2026, 12:17 a.m. |
| NEDg | Description generation | batch_69cc68647cec81909736383fbe73d2e8 |
completed | April 1, 2026, 12:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cc69b93bbc8190be2338182dd57b17 |
completed | April 1, 2026, 12:41 a.m. |
Created at: March 30, 2026, 5:30 p.m.