Triple
T8706829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RMF |
E206670
|
entity |
| Predicate | component |
P35
|
FINISHED |
| Object |
RMF Distributed Data Server
RMF Distributed Data Server is a z/OS component that collects, consolidates, and serves performance and resource usage data from multiple systems for monitoring and analysis.
|
E752532
|
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: RMF Distributed Data Server | Statement: [RMF, component, RMF Distributed Data Server]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RMF Distributed Data Server Context triple: [RMF, component, RMF Distributed Data Server]
-
A.
TimesTen
TimesTen is an in-memory relational database from Oracle designed for extremely low-latency, high-throughput data management and real-time analytics.
-
B.
RethinkDB
RethinkDB is an open-source, distributed NoSQL database designed for real-time applications by pushing live updates to clients as data changes.
-
C.
Caché
Caché is a tributary stream that feeds into the Bléone River in southeastern France.
-
D.
Jepsen
Jepsen is a surname most notably associated with individuals such as display technology innovator Mary Lou Jepsen.
-
E.
Ray Serve
Ray Serve is a scalable model serving library built on the Ray framework that enables deploying and managing machine learning models in production.
- 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: RMF Distributed Data Server Triple: [RMF, component, RMF Distributed Data Server]
Generated description
RMF Distributed Data Server is a z/OS component that collects, consolidates, and serves performance and resource usage data from multiple systems for monitoring and analysis.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: RMF Distributed Data Server Target entity description: RMF Distributed Data Server is a z/OS component that collects, consolidates, and serves performance and resource usage data from multiple systems for monitoring and analysis.
-
A.
TimesTen
TimesTen is an in-memory relational database from Oracle designed for extremely low-latency, high-throughput data management and real-time analytics.
-
B.
RethinkDB
RethinkDB is an open-source, distributed NoSQL database designed for real-time applications by pushing live updates to clients as data changes.
-
C.
Caché
Caché is a tributary stream that feeds into the Bléone River in southeastern France.
-
D.
Jepsen
Jepsen is a surname most notably associated with individuals such as display technology innovator Mary Lou Jepsen.
-
E.
Ray Serve
Ray Serve is a scalable model serving library built on the Ray framework that enables deploying and managing machine learning models in production.
- 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_69ca835645e881908f00e3c8b51da81d |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc58fe19ac8190936ba0faf513ed2b |
completed | March 31, 2026, 11:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf28aac77c8190b4f5968643715765 |
completed | April 3, 2026, 2:40 a.m. |
| NEDg | Description generation | batch_69cf2bcff84881908a7985fdf8189583 |
completed | April 3, 2026, 2:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf2ca1ddac8190a36367e6bba8e3c8 |
completed | April 3, 2026, 2:57 a.m. |
Created at: March 30, 2026, 6:35 p.m.