Triple
T19196478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USRA 4-6-0 Ten‑Wheeler |
E469981
|
entity |
| Predicate | hasLeadingWheelCount |
P19380
|
FINISHED |
| Object | 4 wheels |
—
|
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: 4 wheels | Statement: [USRA 4-6-0 Ten‑Wheeler, hasLeadingWheelCount, 4 wheels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLeadingWheelCount Context triple: [USRA 4-6-0 Ten‑Wheeler, hasLeadingWheelCount, 4 wheels]
-
A.
hasLeadingWheelArrangement
Indicates the specific configuration of the leading (front) wheels in a vehicle’s or locomotive’s wheel arrangement.
-
B.
hasTrailingWheelArrangement
Indicates that an entity (typically a vehicle) possesses a specific configuration of wheels located at its rear or trailing end.
-
C.
hasAxleCount
Indicates the number of axles that an object (typically a vehicle or rolling stock) possesses.
-
D.
numberOfWheels
chosen
Indicates the quantity of wheels that an entity possesses or is associated with.
-
E.
numberOfRoadWheelsPerSide
Indicates the count of road wheels present on each side of a vehicle or similar wheeled system.
- 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_69d8dd0ad9088190a173b32657ae2e7a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f8a5dabc81908fffad811f177b03 |
completed | April 20, 2026, 9:57 a.m. |
| PD | Predicate disambiguation | batch_69e4b9bb158481909478ca2e06f3ba39 |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 12:07 p.m.