Triple
T28521735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Who Made Who? |
E721790
|
entity |
| Predicate | impliesQuestion |
P79744
|
FINISHED |
| Object | who is responsible for creating dangerous machines |
—
|
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: who is responsible for creating dangerous machines | Statement: [Who Made Who?, impliesQuestion, who is responsible for creating dangerous machines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impliesQuestion Context triple: [Who Made Who?, impliesQuestion, who is responsible for creating dangerous machines]
-
A.
implies
Indicates that the truth of one statement guarantees or leads logically to the truth of another statement.
-
B.
questionFormulation
Indicates that one entity formulates, poses, or expresses a question directed toward another entity or context.
-
C.
questionConveyedBy
chosen
Indicates that a particular question is expressed, communicated, or represented by a given medium, message, or artifact.
-
D.
hasImplicationsFor
Indicates that one entity’s state, action, or condition leads to consequences, effects, or relevance for another entity.
-
E.
exploresQuestion
Indicates that an entity actively investigates, examines, or probes a question to gain deeper understanding or insight.
- 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_69f01a5cbcc4819083fb4e723378713e |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f65a6c900881908f18b61273d7bf8d |
completed | May 2, 2026, 8:11 p.m. |
| PD | Predicate disambiguation | batch_69f659ce58408190ba9e007b4810d4d0 |
completed | May 2, 2026, 8:08 p.m. |
Created at: April 28, 2026, 3:21 a.m.