Triple
T30748571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Government of Quezon Province |
E782884
|
entity |
| Predicate | allowedConsecutiveTermsForKeyOfficials |
P155278
|
FINISHED |
| Object | 3 terms |
—
|
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: 3 terms | Statement: [Government of Quezon Province, allowedConsecutiveTermsForKeyOfficials, 3 terms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: allowedConsecutiveTermsForKeyOfficials Context triple: [Government of Quezon Province, allowedConsecutiveTermsForKeyOfficials, 3 terms]
-
A.
numberOfOfficials
Indicates the total count of officials associated with a given entity or context.
-
B.
numberOfTermInOffice
Indicates the specific ordinal count of how many terms an entity has served in a particular office or position.
-
C.
mayServeMultipleTerms
chosen
Indicates that an entity is allowed to hold or perform the same role or position for more than one term.
-
D.
maximumNumberOfTermsForGovernor
Indicates the highest number of terms that a governor is allowed to serve in office.
-
E.
termOfOfficeResult
Indicates the outcome or status associated with a specific term of office, such as whether it was completed, successful, or ended in a particular way.
- 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_69f224af8d8481908bea03890c5618be |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f791cc969c8190bf187d6031a030d5 |
completed | May 3, 2026, 6:19 p.m. |
| PD | Predicate disambiguation | batch_69f791033d288190b118029fe412b9c9 |
completed | May 3, 2026, 6:16 p.m. |
Created at: April 29, 2026, 8:38 p.m.