Triple
T8492805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Solaria |
E201013
|
entity |
| Predicate | robotLawContext |
P83022
|
FINISHED |
| Object | application of the Three Laws of Robotics |
—
|
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: application of the Three Laws of Robotics | Statement: [Solaria, robotLawContext, application of the Three Laws of Robotics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: robotLawContext Context triple: [Solaria, robotLawContext, application of the Three Laws of Robotics]
-
A.
legalAct
Indicates that an entity performs, enacts, or is involved in a formal legal action, measure, or proceeding under a legal framework.
-
B.
obeysLaw
Indicates that an entity follows, complies with, or acts in accordance with a specified law or set of laws.
-
C.
featuresLaw
Indicates that something includes, presents, or is characterized by a particular law or legal provision.
-
D.
legalCodeFocus
Indicates that something is specifically concerned with, centered on, or primarily addressing a particular legal code or body of law.
-
E.
legalContext
Indicates that the relationship or action occurs within, is shaped by, or is relevant to a specific legal framework, proceeding, or set of legal norms.
- 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_69ca831ee390819095fae73400bbfafc |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe55cf5dc81908cad31ac53e15b46 |
completed | March 31, 2026, 3:16 p.m. |
| PD | Predicate disambiguation | batch_69cbd10a4b0881909e254117780dc823 |
completed | March 31, 2026, 1:50 p.m. |
| PDg | Predicate description generation | batch_69cbe30d453481908f897ed2b06e7534 |
completed | March 31, 2026, 3:06 p.m. |
Created at: March 30, 2026, 6:13 p.m.