Triple
T16434032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lion (steam locomotive) |
E399137
|
entity |
| Predicate | hasTrailingWheelArrangement |
P122771
|
FINISHED |
| Object | two trailing 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: two trailing wheels | Statement: [Lion (steam locomotive), hasTrailingWheelArrangement, two trailing wheels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrailingWheelArrangement Context triple: [Lion (steam locomotive), hasTrailingWheelArrangement, two trailing wheels]
-
A.
wheelArrangementSystem
Indicates the specific configuration or system by which the wheels of a vehicle or rolling stock are arranged and organized.
-
B.
numberOfRoadWheelsPerSide
Indicates the count of road wheels present on each side of a vehicle or similar wheeled system.
-
C.
hasAxleCount
Indicates the number of axles that an object (typically a vehicle or rolling stock) possesses.
-
D.
hasNumberOfSpokes
Indicates the relationship that specifies how many spokes are present in or associated with an object.
-
E.
numberOfWheels
Indicates the quantity of wheels that an entity possesses or is associated with.
- F. None of above. chosen
Provenance (4 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32ba01a1c8190b5a4700e2364ae63 |
completed | April 18, 2026, 6:58 a.m. |
| PD | Predicate disambiguation | batch_69e22701d2288190bf8676050758f172 |
completed | April 17, 2026, 12:26 p.m. |
| PDg | Predicate description generation | batch_69e24556c1348190902a4d116c3137d9 |
completed | April 17, 2026, 2:36 p.m. |
Created at: April 10, 2026, 5:10 a.m.