Triple
T37151220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | monorail system (Jurassic World) |
E920366
|
entity |
| Predicate | safetyPerceptionInStory |
P57685
|
FINISHED |
| Object | safe and controlled transportation |
—
|
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: safe and controlled transportation | Statement: [monorail system (Jurassic World), safetyPerceptionInStory, safe and controlled transportation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyPerceptionInStory Context triple: [monorail system (Jurassic World), safetyPerceptionInStory, safe and controlled transportation]
-
A.
safetyPerception
chosen
Indicates how safe an entity is perceived to be by an observer or group, rather than its objectively measured safety.
-
B.
safetyInFiction
Indicates that a work of fiction portrays conditions, measures, or themes related to safety, risk, or protection within its narrative world.
-
C.
safetyImplication
Indicates that one entity has a consequence, effect, or relevance for the safety or risk level associated with another entity or situation.
-
D.
safetyRationale
Indicates the reasoning or justification provided to explain how and why something is considered safe or made safe.
-
E.
safetyResponse
Indicates how an entity reacts or what measures it takes in response to a potential or actual safety-related situation.
- 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_69f76e9f87c08190b4c8f7fafbd8345a |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fba78aca4c8190b8f1831e8cc04e06 |
completed | May 6, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69fba34a65a4819088bac6c17542d71c |
completed | May 6, 2026, 8:23 p.m. |
Created at: May 3, 2026, 4:15 p.m.