Triple
T19871737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peugeot i-Cockpit digital cluster |
E477531
|
entity |
| Predicate | ergonomicGoal |
P49728
|
FINISHED |
| Object | reduce driver eye movement |
—
|
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: reduce driver eye movement | Statement: [Peugeot i-Cockpit digital cluster, ergonomicGoal, reduce driver eye movement]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ergonomicGoal Context triple: [Peugeot i-Cockpit digital cluster, ergonomicGoal, reduce driver eye movement]
-
A.
hasErgonomicType
Indicates that an object or product is associated with a specific ergonomic classification or design type.
-
B.
engineeringGoal
chosen
Indicates that an entity has a specific engineering-related objective, target, or desired outcome it is intended to achieve or support.
-
C.
goalType
Indicates the specific category or nature of a goal associated with an entity or action.
-
D.
orthographicGoal
Indicates that one entity has the intended or target written/orthographic form of another entity.
-
E.
machineGoal
Indicates that a machine or automated system has a specific objective, target state, or outcome it is intended or programmed to achieve.
- 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_69d8e51e7d948190aedbcd6c30361c39 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e658d826f88190be04188997952d1b |
completed | April 20, 2026, 4:48 p.m. |
| PD | Predicate disambiguation | batch_69e537e8c4e481909fe95d795b4864e7 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:51 p.m.