Triple
T35774552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercedes-Benz M102 |
E1034254
|
entity |
| Predicate | cylinderBoreRange |
P25555
|
FINISHED |
| Object | approx. 80–95 mm |
—
|
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: approx. 80–95 mm | Statement: [Mercedes-Benz M102, cylinderBoreRange, approx. 80–95 mm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cylinderBoreRange Context triple: [Mercedes-Benz M102, cylinderBoreRange, approx. 80–95 mm]
-
A.
cylinderBore
chosen
Indicates the diameter of the cylindrical chamber (bore) in which a piston or similar component moves.
-
B.
cylinderCapacity
Indicates the volume or capacity of a cylindrical object, typically specifying how much fluid or gas it can hold.
-
C.
hasBoreType
Indicates that an entity is characterized by or associated with a specific type of bore (e.g., cylindrical, tapered, rifled).
-
D.
numberOfCylinders
Indicates the count of engine cylinders associated with an entity.
-
E.
boreStrokeDesign
Indicates that an engine’s bore and stroke configuration is designed or specified in a particular way.
- 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_69f76e14a1e081908eddd57bd6fdb3be |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7aa699d68819081ed363931894ab3 |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8d219f8819081dc4ce3c83ca0cb |
completed | May 3, 2026, 7:58 p.m. |
Created at: May 3, 2026, 4:06 p.m.