Triple
T18777277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RRS |
E459162
|
entity |
| Predicate | differentFrom |
P1612
|
FINISHED |
| Object | RMS |
—
|
NE NERFINISHED |
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: RMS | Statement: [RRS, differentFrom, RMS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RMS Context triple: [RRS, differentFrom, RMS]
-
A.
RMS
RMS is the stock ticker symbol for Hermès International, the French luxury goods company renowned for its high-end fashion, leather goods, and accessories.
-
B.
RMS
RMS is the vehicle registration code assigned to the municipality of Eppstein in Germany.
-
C.
RMS
RMS is the acronym for Roads and Maritime Services, the former New South Wales government agency responsible for managing roads, traffic, and maritime infrastructure.
-
D.
RMS
chosen
RMS is a common abbreviation that can refer to several entities, most notably the root mean square mathematical measure, the Royal Mail Ship designation for British postal vessels, or the free software advocate Richard M. Stallman, depending on context.
-
E.
RMS Canopic
RMS Canopic was an early 20th-century British ocean liner operated by the White Star Line, used primarily for transatlantic passenger and mail service.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d396f54c8190ba49db31e8743842 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5933c640081909bb6fbaa1a21d411 |
completed | April 20, 2026, 2:45 a.m. |
Created at: April 10, 2026, 11:52 a.m.