Triple
T6151620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R-12 Dvina |
E137215
|
entity |
| Predicate | accuracyCEP |
P62233
|
FINISHED |
| Object | about 2 km |
—
|
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: about 2 km | Statement: [R-12 Dvina, accuracyCEP, about 2 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: accuracyCEP Context triple: [R-12 Dvina, accuracyCEP, about 2 km]
-
A.
accuracyDependsOn
Indicates that the accuracy of one entity or process is contingent upon, or influenced by, another entity or factor.
-
B.
hasAccuracy
chosen
Indicates that something possesses a specified level or measure of correctness, precision, or exactness in relation to a standard or reference.
-
C.
closerTo
Indicates that one entity is at a smaller distance to a reference entity than another entity is.
-
D.
eraLocation
Indicates the place or geographic context in which a particular historical era or time period occurs or is primarily associated.
-
E.
precision
Indicates the degree to which an action, measurement, or outcome is carried out with exactness, minimal deviation, and fine-grained accuracy.
- 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_69c008a45d008190832a9e19f5d63406 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05cfcb5cc8190b998e92211810442 |
completed | March 22, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69c055f39e0881909ae56444b1b48929 |
completed | March 22, 2026, 8:49 p.m. |
Created at: March 22, 2026, 4:16 p.m.