Triple
T29694127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Type I string theory |
E751295
|
entity |
| Predicate | isPerturbativeDescription |
P167802
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Type I string theory, isPerturbativeDescription, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isPerturbativeDescription Context triple: [Type I string theory, isPerturbativeDescription, yes]
-
A.
isPerturbativelyConsistent
Indicates that a theory, model, or approximation remains mathematically well-defined and free of inconsistencies order by order in perturbation theory.
-
B.
hasOrderInPerturbationTheory
Indicates that a given quantity, interaction, or term is associated with a specific order in a perturbative expansion within perturbation theory.
-
C.
isDescribedAtLowEnergyBy
Indicates that one entity serves as the low-energy (effective or approximate) description or theory of another entity.
-
D.
isDescriptive
Indicates that one entity provides a description or characterization of another entity.
-
E.
isRenormalizable
Indicates that a physical theory or interaction can have its infinities systematically absorbed into a finite number of redefined parameters, yielding well-defined, predictive results at all relevant scales.
- 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_69f0d625b09481909b0b69aea1e846c8 |
completed | April 28, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f672af46e48190b76e9298e7d23eef |
completed | May 2, 2026, 9:54 p.m. |
| PD | Predicate disambiguation | batch_69f66ac1a4fc81909740d2e52fbe6970 |
completed | May 2, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69f66c59de9881909ebbb7b0ae7ab495 |
completed | May 2, 2026, 9:27 p.m. |
Created at: April 28, 2026, 7:19 p.m.