Triple
T12109441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sword of Damocles head-mounted display |
E288382
|
entity |
| Predicate | safetyReasonForName |
P7885
|
FINISHED |
| Object | dangerous-looking overhead support |
—
|
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: dangerous-looking overhead support | Statement: [Sword of Damocles head-mounted display, safetyReasonForName, dangerous-looking overhead support]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyReasonForName Context triple: [Sword of Damocles head-mounted display, safetyReasonForName, dangerous-looking overhead support]
-
A.
safetyRationale
Indicates the reasoning or justification provided to explain how and why something is considered safe or made safe.
-
B.
reasonForName
chosen
Indicates the explanation or cause behind why an entity has a particular name.
-
C.
safetyRelevant
Indicates that the associated entity, condition, or information has a direct impact on safety or is critical for preventing harm or accidents.
-
D.
safetyCategory
Indicates the classification of something according to its level or type of safety.
-
E.
safetyConcept
Indicates that something embodies, represents, or is associated with a principle, idea, or framework related to 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_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9164ada5081908676bd9e5947268a |
completed | April 10, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69d9150497408190921334d21503375a |
completed | April 10, 2026, 3:19 p.m. |
Created at: April 8, 2026, 9:49 p.m.