Triple
T27257057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heartland Flyer |
E687650
|
entity |
| Predicate | locomotiveModel |
P86157
|
FINISHED |
| Object | GE P42DC |
—
|
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: GE P42DC | Statement: [Heartland Flyer, locomotiveModel, GE P42DC]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locomotiveModel Context triple: [Heartland Flyer, locomotiveModel, GE P42DC]
-
A.
laterLocomotiveType
Indicates that one locomotive type succeeds or comes after another in time, representing a later development or version in locomotive design.
-
B.
locomotiveWorks
Indicates a relationship where an entity is a facility or company that builds, repairs, or maintains locomotives.
-
C.
locomotiveNumber
Indicates the identifying number assigned to a locomotive in the relationship.
-
D.
typicalLocomotiveClass
Indicates that one locomotive class is the standard or most commonly used class for a given context, operator, or service.
-
E.
notableLocomotiveModels
chosen
Indicates a relationship where specific locomotive models are recognized as notable or significant examples associated with an entity (such as a manufacturer, railway, or time period).
- 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_69ef35567e808190a94458cd44ebff0c |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f626ba4b50819088b7cb438c337786 |
completed | May 2, 2026, 4:30 p.m. |
| PD | Predicate disambiguation | batch_69f620e38aec8190bb184edcdbd6da64 |
completed | May 2, 2026, 4:05 p.m. |
Created at: April 27, 2026, 10:50 a.m.