Triple
T33127755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autoroutes Paris-Rhin-Rhône |
E847764
|
entity |
| Predicate | implementsSafetyMeasures |
P49797
|
FINISHED |
| Object | on its motorway network |
—
|
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: on its motorway network | Statement: [Autoroutes Paris-Rhin-Rhône, implementsSafetyMeasures, on its motorway network]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: implementsSafetyMeasures Context triple: [Autoroutes Paris-Rhin-Rhône, implementsSafetyMeasures, on its motorway network]
-
A.
safeguardingMeasuresInclude
Indicates that certain specific protective or security measures are contained within, or form part of, a broader set of safeguarding measures.
-
B.
measuresSafetyUsing
Indicates that an entity evaluates or assesses safety by employing a specified method, tool, or standard.
-
C.
protectionMeasures
chosen
Indicates actions or safeguards implemented to prevent harm, damage, or risk to someone or something.
-
D.
supportsSafetyChecks
Indicates that an entity provides or enables mechanisms for performing safety checks or validations.
-
E.
hasSafetyCharacteristic
Indicates that an entity possesses a specific safety-related property, feature, or attribute.
- 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_69f349588f088190b7c9588860f72033 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6f38159d08190980ad639e08f00f4 |
completed | May 3, 2026, 7:04 a.m. |
| PD | Predicate disambiguation | batch_69f6e3d7bee48190b94e0beb48a1d7fa |
completed | May 3, 2026, 5:57 a.m. |
Created at: May 1, 2026, 1:27 a.m.