Triple
T4510982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | California State Route 190 |
E102052
|
entity |
| Predicate | hasTerminusArea |
P39212
|
FINISHED |
| Object | western Sierra Nevada region |
—
|
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: western Sierra Nevada region | Statement: [California State Route 190, hasTerminusArea, western Sierra Nevada region]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTerminusArea Context triple: [California State Route 190, hasTerminusArea, western Sierra Nevada region]
-
A.
hasTerminusIn
chosen
Indicates that something (such as a route, line, or path) ends or has its final stopping point at a specified location.
-
B.
hasStopArea
Indicates that an entity is associated with or contains a specific stop area, such as a designated location where vehicles stop.
-
C.
hasBorderTerminus
Indicates that one entity serves as the endpoint or terminal location of another entity’s border or boundary.
-
D.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
E.
hasTerminalFacility
Indicates that an entity possesses or includes a terminal facility used as an endpoint for transport, communication, or related operations.
- 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_69bd43d6251c81909deecce3e6e9d69c |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5712b9308190876c117b50d12635 |
completed | March 20, 2026, 2:17 p.m. |
| PD | Predicate disambiguation | batch_69bd5218afb4819087c99e0a1f22e137 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:01 p.m.