Triple
T8492852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Three Laws of Robotics |
E201014
|
entity |
| Predicate | orderOfPriority |
P37518
|
FINISHED |
| Object | First Law supersedes Second Law |
—
|
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: First Law supersedes Second Law | Statement: [Three Laws of Robotics, orderOfPriority, First Law supersedes Second Law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: orderOfPriority Context triple: [Three Laws of Robotics, orderOfPriority, First Law supersedes Second Law]
-
A.
limitsPriorityOf
Indicates that one entity imposes a maximum allowable priority level on another entity’s priority.
-
B.
orderOf
chosen
Indicates that one entity is arranged, ranked, or sequenced before or after another according to a specified ordering criterion.
-
C.
planningPriority
Indicates the relative importance or urgency assigned to an item, task, or objective when organizing or scheduling plans.
-
D.
prioritises
Indicates that one entity gives greater importance, preference, or precedence to another entity or task over alternatives.
-
E.
managementPriority
Indicates the relative level of importance or urgency assigned to managing or addressing a particular entity, issue, or task.
- 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_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. |
Created at: March 30, 2026, 6:13 p.m.