Triple
T2978014
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercedes-Benz EQS |
E80443
|
entity |
| Predicate | targetAttribute |
P15065
|
FINISHED |
| Object | long driving range |
—
|
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: long driving range | Statement: [Mercedes-Benz EQS, targetAttribute, long driving range]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetAttribute Context triple: [Mercedes-Benz EQS, targetAttribute, long driving range]
-
A.
attributeType
Indicates that one entity specifies the kind or category of attribute that characterizes another entity.
-
B.
target
Indicates that one entity is the intended object, goal, or focus of another entity’s action or attention.
-
C.
brandAttribute
chosen
Indicates that a specific attribute or characteristic is associated with, or describes, a particular brand.
-
D.
usesAttribute
Indicates that one entity employs, relies on, or makes use of a specific attribute of another entity in performing an action or defining a relationship.
-
E.
targetArea
Indicates the specific area or region that is the intended focus or destination of an action or effect.
- 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_69ad8b15f6ac8190be5fd16a33edcb4f |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad999b0d50819093dac7678b887a9b |
completed | March 8, 2026, 3:45 p.m. |
| PD | Predicate disambiguation | batch_69ad96105a708190a9ec4838cbcb1207 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:58 p.m.