Triple
T20836532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cook–Levin theorem |
E512972
|
entity |
| Predicate | assumesModel |
P1458
|
FINISHED |
| Object | Turing machine model of computation |
—
|
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: Turing machine model of computation | Statement: [Cook–Levin theorem, assumesModel, Turing machine model of computation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: assumesModel Context triple: [Cook–Levin theorem, assumesModel, Turing machine model of computation]
-
A.
assumes
chosen
Indicates that one entity takes on, accepts, or presumes a role, responsibility, state, or fact regarding another entity or situation.
-
B.
possibleModel
Indicates that one entity can serve as a potential or candidate model or template for another entity.
-
C.
adoptedModel
Indicates that one entity has formally chosen, accepted, or implemented another entity as its preferred model or standard.
-
D.
modeledWith
Indicates that something is represented, simulated, or described using a particular model, method, or modeling technique.
-
E.
appliesModel
Indicates that one entity uses or executes a specific model on another entity or data set.
- 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_69e0b4cf62a88190bbf92351e9e57259 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c326daec8190bd4caa41a4b38833 |
completed | April 21, 2026, 12:21 a.m. |
| PD | Predicate disambiguation | batch_69e5c9a1f4f48190aa9fb4ef8f8aea5a |
completed | April 20, 2026, 6:37 a.m. |
Created at: April 16, 2026, 12:42 p.m.