Triple
T6305284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 3GPP RAN |
E141359
|
entity |
| Predicate | hasWorkingGroup |
P1382
|
FINISHED |
| Object | RAN5 |
E23872
|
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: RAN5 | Statement: [3GPP RAN, hasWorkingGroup, RAN5]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RAN5 Context triple: [3GPP RAN, hasWorkingGroup, RAN5]
-
A.
RAN
The Royal Australian Navy (RAN) is the naval branch of the Australian Defence Force, responsible for conducting maritime military operations and protecting Australia's maritime interests.
-
B.
RAN
chosen
RAN is the 3GPP working group responsible for specifying the radio access network technologies used in mobile communication systems such as LTE and 5G.
-
C.
R5
R5 is the U.S. Forest Service’s Pacific Southwest Region, which oversees national forests primarily in California and parts of neighboring areas.
-
D.
R5
R5 is a government office building in Oslo that forms part of Norway’s central Regjeringskvartalet complex.
-
E.
R55
R55 is a regional road in South Africa that runs through Gauteng, connecting areas such as Midrand with surrounding suburbs and major routes.
- 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_69c008cf0ad4819095def81e2bd42f9f |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06479acec819090306a155a03b774 |
completed | March 22, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c603feee388190921239cda3772210 |
completed | March 27, 2026, 4:13 a.m. |
Created at: March 22, 2026, 4:28 p.m.