Triple
T13228106
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercedes-Benz EQV |
E314932
|
entity |
| Predicate | widthApproximate |
P18961
|
FINISHED |
| Object | 1928 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: 1928 mm | Statement: [Mercedes-Benz EQV, widthApproximate, 1928 mm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: widthApproximate Context triple: [Mercedes-Benz EQV, widthApproximate, 1928 mm]
-
A.
heightApproximateFeet
Indicates that one entity’s height is approximately equal to a specified value measured in feet.
-
B.
hasDimensionsApprox
chosen
Indicates that an entity has physical dimensions that are known only approximately, rather than as exact measurements.
-
C.
approximateWeightInPounds
Indicates the estimated weight of an entity expressed in pounds, rather than an exact measured value.
-
D.
width
Indicates the measurement of how wide an entity is, typically the extent of its horizontal dimension from side to side.
-
E.
approximateDiameter
Indicates that one entity specifies the estimated or rough measurement of another entity’s diameter.
- 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_69d806affc688190a25b6ccc588e9c72 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d3232d48190a3c792b025c596a6 |
completed | April 10, 2026, 11:52 p.m. |
| PD | Predicate disambiguation | batch_69d98bcb21648190aef241de1e7887e2 |
completed | April 10, 2026, 11:46 p.m. |
Created at: April 9, 2026, 9:21 p.m.