Triple
T32278196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nicaraguan general election, 2016 |
E824616
|
entity |
| Predicate | resultOrtegaOverallTermCount |
P9908
|
FINISHED |
| Object | fourth overall presidential term |
—
|
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: fourth overall presidential term | Statement: [Nicaraguan general election, 2016, resultOrtegaOverallTermCount, fourth overall presidential term]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: resultOrtegaOverallTermCount Context triple: [Nicaraguan general election, 2016, resultOrtegaOverallTermCount, fourth overall presidential term]
-
A.
resultOrtegaTermNumber
Indicates that an outcome or result is associated with a specific Ortega term identified by its term number.
-
B.
hasNumberOfTerms
chosen
Indicates the quantity of distinct terms or elements associated with a given entity or expression.
-
C.
bestResultsCounted
Indicates that the number of best or top-performing results in a given context has been determined and recorded.
-
D.
maximumTermCount
Indicates the highest number of terms that are allowed or considered within a given context or operation.
-
E.
opposedNumberOfTerms
Indicates that two entities are in opposition with respect to the number of terms they involve or are associated with.
- 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_69f3490f404081908450db66884f4334 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fba78aca4c8190b8f1831e8cc04e06 |
completed | May 6, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69fba34a65a4819088bac6c17542d71c |
completed | May 6, 2026, 8:23 p.m. |
Created at: May 1, 2026, 12:43 a.m.