Triple
T1211367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ford Model N |
E26006
|
entity |
| Predicate | wheelMaterial |
P1272
|
FINISHED |
| Object | wooden artillery 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: wooden artillery wheels | Statement: [Ford Model N, wheelMaterial, wooden artillery wheels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wheelMaterial Context triple: [Ford Model N, wheelMaterial, wooden artillery wheels]
-
A.
wheelType
Indicates the specific kind or category of wheel associated with an entity.
-
B.
materialUsed
chosen
Indicates that one entity is made from, incorporates, or utilizes the other entity as its material or substance.
-
C.
exteriorMaterial
Indicates the material that forms the outer surface or outer construction of an object or structure.
-
D.
chassisMaterialFeature
Indicates that an entity has a chassis characterized by a specific material-related feature or property.
-
E.
numberOfSpokes
Indicates the count of individual spokes associated with or contained in a given object or structure.
- 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_69a4948331fc8190b531ac9bec71c491 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bde581308190bbe30683bf6c48c3 |
completed | March 1, 2026, 10:29 p.m. |
| PD | Predicate disambiguation | batch_69a4bb62a7c08190a79dcb6ff72ac99b |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:46 p.m.