Triple
T26459713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amtrak Carl Sandburg |
E665594
|
entity |
| Predicate | operatingSpeedCategory |
P9902
|
FINISHED |
| Object | conventional rail |
—
|
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: conventional rail | Statement: [Amtrak Carl Sandburg, operatingSpeedCategory, conventional rail]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operatingSpeedCategory Context triple: [Amtrak Carl Sandburg, operatingSpeedCategory, conventional rail]
-
A.
approximateOperatingSpeed
Indicates that one entity specifies or characterizes the estimated or typical operating speed of another entity, rather than an exact value.
-
B.
topOperatingSpeed
Indicates the maximum speed at which an entity is designed or allowed to operate under normal conditions.
-
C.
speedClass
chosen
Indicates the categorical speed level or range assigned to an entity based on how fast it moves or operates.
-
D.
marketedSpeedName
Indicates the branded or advertised name used to describe the speed of a product or service.
-
E.
recordSpeedCategory
Indicates the classification of an entity’s speed into a predefined category (e.g., slow, normal, fast) based on its recorded speed.
- 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_69ee883e812c8190a9b5a9cdb87fee5e |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69fd44474ed48190ac372e4c88d762ed |
completed | May 8, 2026, 2:02 a.m. |
| PD | Predicate disambiguation | batch_69fd41ef28a48190a66959be5c964461 |
completed | May 8, 2026, 1:52 a.m. |
Created at: April 27, 2026, 12:11 a.m.