Triple
T1078158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Superliner bi-level cars |
E23884
|
entity |
| Predicate | primaryRegionOfOperation |
P6665
|
FINISHED |
| Object | western United States |
—
|
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 United States | Statement: [Superliner bi-level cars, primaryRegionOfOperation, western United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryRegionOfOperation Context triple: [Superliner bi-level cars, primaryRegionOfOperation, western United States]
-
A.
operatedInRegion
chosen
Indicates that an entity conducted operations or activities within a specified geographic region.
-
B.
coreRegionOf
Indicates that one entity constitutes the central or most essential part of another entity’s structure, area, or domain.
-
C.
primaryTerritory
Indicates that a specified area or region is the main or most important territory associated with an entity.
-
D.
hasTypicalUsageRegion
Indicates that something is most commonly or characteristically used within a particular geographic region.
-
E.
operatesInCountries
Indicates that an entity conducts its activities or business within the specified countries.
- 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_69a493f1ddf48190a99d54b00e99f8ce |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b94288d88190aae4fb86236c0702 |
completed | March 1, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69a4b73ba8208190be7f3cef8c18689b |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.