Triple
T24306920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morning Hiawatha |
E612559
|
entity |
| Predicate | usedLocomotiveClass |
P110444
|
FINISHED |
| Object | Milwaukee Road class F7 Hudson |
—
|
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: Milwaukee Road class F7 Hudson | Statement: [Morning Hiawatha, usedLocomotiveClass, Milwaukee Road class F7 Hudson]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedLocomotiveClass Context triple: [Morning Hiawatha, usedLocomotiveClass, Milwaukee Road class F7 Hudson]
-
A.
usedLocomotives
chosen
Indicates that an entity employed locomotives as tools, resources, or means to perform an action or fulfill a function.
-
B.
typicalLocomotiveClass
Indicates that one locomotive class is the standard or most commonly used class for a given context, operator, or service.
-
C.
laterLocomotiveType
Indicates that one locomotive type succeeds or comes after another in time, representing a later development or version in locomotive design.
-
D.
primaryLocomotiveType
Indicates the main method or mechanism by which an entity typically moves or travels.
-
E.
hasLocomotive
Indicates that one entity possesses or is equipped with a locomotive as part of its composition or operation.
- 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_69e2d7d91bb48190bc5377d17a85fb21 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f292272af88190b9c23615adbac911 |
completed | April 29, 2026, 11:20 p.m. |
| PD | Predicate disambiguation | batch_69f1c45c6ec081908401b69424428100 |
completed | April 29, 2026, 8:42 a.m. |
Created at: April 18, 2026, 1:31 a.m.