Triple
T6114974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hiromi Uehara |
E136336
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Spark
"Spark" is a virtuosic jazz fusion composition by Japanese pianist Hiromi Uehara, showcasing her signature blend of technical brilliance and energetic, genre-blurring style.
|
E568279
|
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: Spark | Statement: [Hiromi Uehara, notableWork, Spark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spark Context triple: [Hiromi Uehara, notableWork, Spark]
-
A.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
-
B.
Apache Pig
Apache Pig is a high-level platform for creating MapReduce programs used to analyze large data sets in the Hadoop ecosystem.
-
C.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
D.
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.
-
E.
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.
- 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: Spark Triple: [Hiromi Uehara, notableWork, Spark]
Generated description
"Spark" is a virtuosic jazz fusion composition by Japanese pianist Hiromi Uehara, showcasing her signature blend of technical brilliance and energetic, genre-blurring style.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Spark Target entity description: "Spark" is a virtuosic jazz fusion composition by Japanese pianist Hiromi Uehara, showcasing her signature blend of technical brilliance and energetic, genre-blurring style.
-
A.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
-
B.
Apache Pig
Apache Pig is a high-level platform for creating MapReduce programs used to analyze large data sets in the Hadoop ecosystem.
-
C.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
D.
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.
-
E.
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.
- 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_69c0089ea6f88190b349be53e04b4f5f |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05bc0bee08190ab93eae34ea8cdde |
completed | March 22, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1256aa9f8819090fdc98343af6f8e |
completed | March 23, 2026, 11:35 a.m. |
| NEDg | Description generation | batch_69c126dd44888190b20a02ede3705918 |
completed | March 23, 2026, 11:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c1276a279c8190a6833c8c986e1291 |
completed | March 23, 2026, 11:43 a.m. |
Created at: March 22, 2026, 4:14 p.m.