Triple
T14831159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | President of Fiji |
E348706
|
entity |
| Predicate | numberOfTermsLimit |
P49810
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [President of Fiji, numberOfTermsLimit, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTermsLimit Context triple: [President of Fiji, numberOfTermsLimit, 2]
-
A.
maximumTermCount
chosen
Indicates the highest number of terms that are allowed or considered within a given context or operation.
-
B.
hasNumberOfTerms
Indicates the quantity of distinct terms or elements associated with a given entity or expression.
-
C.
maximumNumberOfTermsForGovernor
Indicates the highest number of terms that a governor is allowed to serve in office.
-
D.
definesLimitOf
Indicates that one entity specifies or establishes the maximum or boundary condition applicable to another entity.
-
E.
maximumConsecutiveTerms
Indicates the greatest number of terms that can occur in an unbroken, continuous sequence within a given context or structure.
- 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_69d822ec69008190a9232caa68836872 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded0748eec8190a39c94024c3a3e3e |
completed | April 14, 2026, 11:40 p.m. |
| PD | Predicate disambiguation | batch_69de8c13418c819088ff9905ace1416a |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:52 a.m.