Triple
T34518497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States presidential system |
E886218
|
entity |
| Predicate | servesAsModelFor |
P28233
|
FINISHED |
| Object | many Latin American presidential 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: many Latin American presidential systems | Statement: [United States presidential system, servesAsModelFor, many Latin American presidential systems]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesAsModelFor Context triple: [United States presidential system, servesAsModelFor, many Latin American presidential systems]
-
A.
modeledWith
Indicates that something is represented, simulated, or described using a particular model, method, or modeling technique.
-
B.
isModelOf
chosen
Indicates that one entity serves as a representation or abstraction that captures the structure or behavior of another entity.
-
C.
servesAsQualifierFor
Indicates that one entity functions as a qualifier or modifier that refines, restricts, or specifies the meaning or scope of another entity.
-
D.
usedByModel
Indicates that something (such as a resource, method, or component) is utilized or consumed by a particular model.
-
E.
introducedAsModel
Indicates that one entity is presented or identified to others in the role or capacity of a model.
- 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_69f349ccc290819089d8e82698e53cb6 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ffb82be8148190a1c870d467a28c80 |
completed | May 9, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69ffb7bbd550819094052e9a0d0ae320 |
completed | May 9, 2026, 10:39 p.m. |
Created at: May 1, 2026, 2:01 a.m.