Triple
T14538549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shenandoah GC |
E341112
|
entity |
| Predicate | maintainedBy |
P86
|
FINISHED |
| Object | Red Hat |
E5668
|
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: Red Hat | Statement: [Shenandoah GC, maintainedBy, Red Hat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Red Hat Context triple: [Shenandoah GC, maintainedBy, Red Hat]
-
A.
Red Hat
chosen
Red Hat is a leading American open-source software company best known for its enterprise Linux distribution and related cloud and middleware solutions.
-
B.
Red Hat Enterprise Linux
Red Hat Enterprise Linux is a commercially supported, enterprise-grade Linux distribution widely used for servers, cloud deployments, and mission-critical applications.
-
C.
SUSE
SUSE is a German-based open-source software company best known for its enterprise Linux distributions and related infrastructure solutions.
-
D.
Azul Systems
Azul Systems is a software company specializing in high-performance, scalable Java runtimes and JVM technologies for enterprise applications.
-
E.
Novell
Novell was a prominent software company best known for its NetWare network operating system and contributions to enterprise networking and Linux technologies.
- 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_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb1bb90008190947ac0961393446d |
completed | April 14, 2026, 9:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd7a5cca788190aa8762d860c78721 |
completed | May 8, 2026, 5:53 a.m. |
Created at: April 10, 2026, 1:22 a.m.