Triple
T32258364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NuSMV |
E824080
|
entity |
| Predicate | hasInputLanguage |
P9278
|
FINISHED |
| Object | SMV-like modeling language |
—
|
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: SMV-like modeling language | Statement: [NuSMV, hasInputLanguage, SMV-like modeling language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInputLanguage Context triple: [NuSMV, hasInputLanguage, SMV-like modeling language]
-
A.
hasLanguageType
Indicates that an entity is associated with a particular type or category of language (e.g., spoken, written, programming, sign).
-
B.
hasLanguageStatus
Indicates that an entity has a particular status or condition regarding its language use, recognition, or classification.
-
C.
usesLanguageSupport
Indicates that one entity makes use of language-related assistance, features, or services provided by another entity.
-
D.
hasLanguageOn
chosen
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
-
E.
hasExplicitLanguage
Indicates that the subject contains or uses language that is coarse, profane, or otherwise unsuitable for certain audiences.
- 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_69f3490db0748190bfef6e50c95d39d3 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69ff8aff48988190a48a440de8238ef9 |
completed | May 9, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69ff8a780404819082f48ceb21e7fe11 |
completed | May 9, 2026, 7:26 p.m. |
Created at: May 1, 2026, 12:41 a.m.