Triple
T33671992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Halden research reactor |
E862641
|
entity |
| Predicate | safetyResearch |
P179637
|
FINISHED |
| Object | human factors and control room design |
—
|
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: human factors and control room design | Statement: [Halden research reactor, safetyResearch, human factors and control room design]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyResearch Context triple: [Halden research reactor, safetyResearch, human factors and control room design]
-
A.
safety
Indicates that an entity provides, ensures, or is associated with protection from harm, danger, or risk for another entity or within a given context.
-
B.
safetyRelevant
Indicates that the associated entity, condition, or information has a direct impact on safety or is critical for preventing harm or accidents.
-
C.
safetyCategory
Indicates the classification of something according to its level or type of safety.
-
D.
safetyConcept
Indicates that something embodies, represents, or is associated with a principle, idea, or framework related to safety.
-
E.
safetyInnovationBy
Indicates that a safety-related innovation, measure, or improvement is created, introduced, or implemented by a specific agent or entity.
- 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_69f34985885c8190914322f492e04703 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7238172748190b8cd340ad1f4ba80 |
completed | May 3, 2026, 10:29 a.m. |
| PD | Predicate disambiguation | batch_69f72155c48881909bd40b9aa3febd5a |
completed | May 3, 2026, 10:20 a.m. |
| PDg | Predicate description generation | batch_69f72349f1108190b6a06758ab2f40bb |
completed | May 3, 2026, 10:28 a.m. |
Created at: May 1, 2026, 1:43 a.m.