Triple
T969804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DE30AC locomotive |
E20919
|
entity |
| Predicate | hasCabConfiguration |
P13644
|
FINISHED |
| Object | full-width cowl cab |
—
|
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: full-width cowl cab | Statement: [DE30AC locomotive, hasCabConfiguration, full-width cowl cab]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCabConfiguration Context triple: [DE30AC locomotive, hasCabConfiguration, full-width cowl cab]
-
A.
hasConfiguration
chosen
Indicates that an entity is associated with or defined by a particular configuration or setup.
-
B.
hasCP
Indicates that an entity possesses, is associated with, or is characterized by a specific CP (such as a control point, contact person, or configuration parameter), depending on the domain context.
-
C.
hasSetting
Indicates that an entity takes place, occurs, or exists within a particular environment, context, or location.
-
D.
hasCabinet
Indicates that one entity possesses, includes, or is equipped with a cabinet associated with it.
-
E.
hasRunwayConfiguration
Indicates a specific arrangement or setup of runways associated with an airport, airfield, or similar facility.
- 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b4497d688190b59c3a195e377080 |
completed | March 1, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a579888190afb489ac9fe8391c |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.