Triple
T5121006
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peugeot 106 |
E115461
|
entity |
| Predicate | safetyEquipmentLaterModels |
P60327
|
FINISHED |
| Object | driver airbag |
—
|
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: driver airbag | Statement: [Peugeot 106, safetyEquipmentLaterModels, driver airbag]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyEquipmentLaterModels Context triple: [Peugeot 106, safetyEquipmentLaterModels, driver airbag]
-
A.
safetyEquipment
Indicates that one entity serves as safety equipment used to protect another entity from harm or danger.
-
B.
protectiveEquipment
Indicates that one entity serves as protective equipment used to safeguard another entity from harm or risk.
-
C.
laterEquipmentType
chosen
Indicates that one equipment type occurs or is used at a later time relative to another equipment type.
-
D.
helmetType
Indicates the specific category or style of helmet associated with an entity.
-
E.
safetyCategory
Indicates the classification of something according to its level or type of safety.
- 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_69bd4442ade0819087b9461f892b206b |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7d5a23908190a24e79d1b29d6fcf |
completed | March 20, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69bd77aa68b88190a50dd736a72d2901 |
completed | March 20, 2026, 4:36 p.m. |
Created at: March 20, 2026, 1:42 p.m.