Triple
T31357879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Humanoids |
E799784
|
entity |
| Predicate | hasRobotLawsVariant |
P83024
|
FINISHED |
| Object | non-Asimovian protective rules |
—
|
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: non-Asimovian protective rules | Statement: [The Humanoids, hasRobotLawsVariant, non-Asimovian protective rules]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRobotLawsVariant Context triple: [The Humanoids, hasRobotLawsVariant, non-Asimovian protective rules]
-
A.
hasRobotLaw
chosen
Indicates that an entity is governed by, complies with, or is associated with a specific law or set of laws regulating robots or robotic behavior.
-
B.
robotLawContext
Indicates the legal, ethical, or regulatory framework within which a robot’s actions, rights, or obligations are defined or evaluated.
-
C.
hasRobot
Indicates that one entity possesses, controls, or is associated with a robot.
-
D.
hasRobotPolicy
Indicates that an entity has an established policy governing the behavior, use, or management of robots.
-
E.
hasAutonomousLaw
Indicates that an entity possesses its own independent legal authority or system of laws, not subordinated to another jurisdiction.
- 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_69f224e5e9bc8190a16339328897c4f8 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fd3d46d1f48190a1b20dd063224b7d |
completed | May 8, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69fd3ae1510c81908fe1280efc17feee |
completed | May 8, 2026, 1:22 a.m. |
Created at: April 29, 2026, 9:17 p.m.