Triple
T37612758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vice President of Palau |
E935831
|
entity |
| Predicate | officeHoldersTermType |
P202095
|
FINISHED |
| Object | fixed 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: fixed term | Statement: [Vice President of Palau, officeHoldersTermType, fixed term]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeHoldersTermType Context triple: [Vice President of Palau, officeHoldersTermType, fixed term]
-
A.
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.
-
B.
parliamentaryTermType
Indicates the specific category or classification of a parliamentary term within a legislative or governmental context.
-
C.
legislativeTermType
Indicates the specific category or classification of a legislative term within a legislative body or system.
-
D.
termInOffice
Indicates the period during which an individual officially holds a particular office or position.
-
E.
numberOfTermInOffice
Indicates the specific ordinal count of how many terms an entity has served in a particular office or position.
- F. None of above. chosen
Provenance (4 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_69f76ed16b748190ad6add183b1be688 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a004fd3caf48190b2ec063a7bf0756b |
completed | May 10, 2026, 9:28 a.m. |
| PD | Predicate disambiguation | batch_6a004f7672dc8190aca91d1ed855bf9a |
completed | May 10, 2026, 9:27 a.m. |
| PDg | Predicate description generation | batch_6a004fd327788190a4c96160cb86d154 |
completed | May 10, 2026, 9:28 a.m. |
Created at: May 3, 2026, 4:18 p.m.