Triple
T9566890
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CCS |
E230808
|
entity |
| Predicate | typicalModel |
P2006
|
FINISHED |
| Object | concurrent systems |
—
|
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: concurrent systems | Statement: [CCS, typicalModel, concurrent systems]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalModel Context triple: [CCS, typicalModel, concurrent systems]
-
A.
typicalBase
Indicates that one entity serves as the standard or most representative base or foundation for another entity in typical or common cases.
-
B.
typicalIn
Indicates that something commonly occurs, appears, or is found within a given context, category, or environment.
-
C.
typicalCoreType
Indicates that something is a standard or characteristic core type within a given classification or system.
-
D.
possibleModel
Indicates that one entity can serve as a potential or candidate model or template for another entity.
-
E.
model
chosen
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
- 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_69ca847f22188190a56e4a97625bef22 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd996df4f08190b19bbaefb10a9789 |
completed | April 1, 2026, 10:17 p.m. |
| PD | Predicate disambiguation | batch_69ccd59b960c8190966a8870a2426bd5 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:04 p.m.