Triple
T16926184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AP1000 |
E410578
|
entity |
| Predicate | activeSafetySystemReduction |
P33946
|
FINISHED |
| Object | reduced number of active safety components |
—
|
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: reduced number of active safety components | Statement: [AP1000, activeSafetySystemReduction, reduced number of active safety components]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: activeSafetySystemReduction Context triple: [AP1000, activeSafetySystemReduction, reduced number of active safety components]
-
A.
safetyRelevant
Indicates that the associated entity, condition, or information has a direct impact on safety or is critical for preventing harm or accidents.
-
B.
safetyBenefit
Indicates that one entity provides, contributes to, or results in an improvement in the safety or risk reduction experienced by another entity.
-
C.
safetyChangesImplemented
chosen
Indicates that specific safety-related modifications or measures have been put into effect.
-
D.
safetyPerception
Indicates how safe an entity is perceived to be by an observer or group, rather than its objectively measured safety.
-
E.
safetyRatingHighlight
Indicates that an entity’s safety rating is emphasized or specially marked as noteworthy compared to others.
- 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_69d886c7b1e481908c3766dfa8c13458 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3cdf2dcd881909798bd245e18c599 |
completed | April 18, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69e32b982f548190b08414d55810de19 |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:30 a.m.