Triple
T18773791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Victoria Rupes |
E459081
|
entity |
| Predicate | featureTypeTermOrigin |
P2530
|
FINISHED |
| Object | Rupes is Latin for cliff |
—
|
LITERAL 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: Rupes is Latin for cliff | Statement: [Victoria Rupes, featureTypeTermOrigin, Rupes is Latin for cliff]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featureTypeTermOrigin Context triple: [Victoria Rupes, featureTypeTermOrigin, Rupes is Latin for cliff]
-
A.
etymologyType
chosen
Indicates the specific kind or category of etymological relationship that links a term to its linguistic origin or source.
-
B.
languageOfCoinedTerm
Indicates the language in which a particular term was originally coined or first formulated.
-
C.
speciesOrigin
Indicates the place, environment, or source from which a species originally arose or evolved.
-
D.
hasAcronymOrigin
Indicates that an acronym is derived from or originates from a specific longer expression or name.
-
E.
historicalOriginMeaning
Indicates that one entity explains the original historical source or derivational meaning of another entity.
- F. None of above.
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_69d8d395dba0819087568404508590cb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e593378a4081909cc1b0856fce7d7d |
completed | April 20, 2026, 2:45 a.m. |
| PD | Predicate disambiguation | batch_69e48d1126e4819099607837ed5aadca |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:52 a.m.