Triple
T16495998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 45562 Alberta |
E400685
|
entity |
| Predicate | framesReusedFor |
P91997
|
FINISHED |
| Object | another locomotive |
—
|
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: another locomotive | Statement: [45562 Alberta, framesReusedFor, another locomotive]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: framesReusedFor Context triple: [45562 Alberta, framesReusedFor, another locomotive]
-
A.
framesAs
Indicates how one entity presents, characterizes, or interprets another entity or situation in a particular light or context.
-
B.
usedReferenceFrame
Indicates that one entity adopts or relies on another entity as the coordinate system or reference frame for describing positions, motions, or measurements.
-
C.
reusedIn
chosen
Indicates that something previously used in one context or instance is used again in another context or instance.
-
D.
partiallyReusedAs
Indicates that one entity is used again as part of another entity, but only to a limited or incomplete extent rather than in its entirety.
-
E.
numberOfReconstructions
Indicates the count of times an entity has been reconstructed or rebuilt.
- 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_69d88381f6148190819958a038be990e |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e332c1c8190888a042d0192233a |
completed | April 18, 2026, 7:09 a.m. |
| PD | Predicate disambiguation | batch_69e296902d6c8190884ddb612b8c5b36 |
completed | April 17, 2026, 8:22 p.m. |
Created at: April 10, 2026, 5:14 a.m.