Triple
T13043516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lexus IS |
E327256
|
entity |
| Predicate | safetyRatingHighlight |
P108412
|
FINISHED |
| Object | high crash-test ratings in several markets |
—
|
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: high crash-test ratings in several markets | Statement: [Lexus IS, safetyRatingHighlight, high crash-test ratings in several markets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyRatingHighlight Context triple: [Lexus IS, safetyRatingHighlight, high crash-test ratings in several markets]
-
A.
safetyCategory
Indicates the classification of something according to its level or type of safety.
-
B.
safetyPoints
Indicates a relationship where an entity is assigned or associated with a measure of safety, typically quantified as points reflecting its safety level or performance.
-
C.
safetyIssueHighlighted
Indicates that a particular safety concern or hazard has been identified and explicitly brought to attention.
-
D.
safetyProfile
Indicates the overall level and characteristics of risk or harm associated with something, typically summarizing how safe it is under specified conditions.
-
E.
safetyPerception
Indicates how safe an entity is perceived to be by an observer or group, rather than its objectively measured safety.
- F. None of above. chosen
Provenance (4 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d98a9829b48190b23624b6b3df4600 |
completed | April 10, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69d9803aca4c8190b1015cd159cc47a9 |
completed | April 10, 2026, 10:56 p.m. |
| PDg | Predicate description generation | batch_69d98a9577d081908ddef9ea77e408e2 |
completed | April 10, 2026, 11:41 p.m. |
Created at: April 9, 2026, 8:56 p.m.