Triple
T24638050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EMD 645-series diesel engine |
E609867
|
entity |
| Predicate | typicalApplicationsRegion |
P908
|
FINISHED |
| Object | North America |
—
|
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: North America | Statement: [EMD 645-series diesel engine, typicalApplicationsRegion, North America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalApplicationsRegion Context triple: [EMD 645-series diesel engine, typicalApplicationsRegion, North America]
-
A.
hasTypicalUsageRegion
Indicates that something is most commonly or characteristically used within a particular geographic region.
-
B.
typicalRegionType
Indicates that a region is of a characteristic or commonly occurring type for a given context or entity.
-
C.
usedInRegion
chosen
Indicates that something is utilized or applied within a specific geographic or administrative region.
-
D.
usesRegionType
Indicates that one entity operates within, is configured for, or otherwise makes use of a specified type of region.
-
E.
appliesToRegionType
Indicates that something is relevant or applicable specifically to a particular type or category of region.
- 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_69e2c4d28f848190ac38c400060e943d |
completed | April 17, 2026, 11:40 p.m. |
| NER | Named-entity recognition | batch_69f2be064ff88190b5d9e5ec75a41242 |
completed | April 30, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f2a6d0ab708190b2e3b94dd20ca76b |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:33 a.m.