Triple
T11674932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Presidential Office Building, Taipei |
E277466
|
entity |
| Predicate | openedAsGovernorGeneralOffice |
P100718
|
FINISHED |
| Object | 1919 |
—
|
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: 1919 | Statement: [Presidential Office Building, Taipei, openedAsGovernorGeneralOffice, 1919]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: openedAsGovernorGeneralOffice Context triple: [Presidential Office Building, Taipei, openedAsGovernorGeneralOffice, 1919]
-
A.
startTimeAsGovernorGeneral
Indicates the point in time when an individual began serving in the role of Governor General.
-
B.
orderInOfficeAsGovernorGeneralOfCanada
Indicates the numerical sequence in which someone has held the office of Governor General of Canada.
-
C.
governedUnderGovernorGeneral
Indicates that an entity is administered or ruled under the authority of a Governor General as its representative head of governance.
-
D.
officeStart (Governor-General of Pakistan)
Indicates the date or point in time when the individual began serving as Governor-General of Pakistan.
-
E.
madeGovernorGeneralOfBengal
Indicates that a person was appointed to the position of Governor-General of Bengal.
- 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_69d6aafd0a448190b44da30af8c6c519 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a44504c48190b519765a83ff9c5e |
completed | April 10, 2026, 7:18 a.m. |
| PD | Predicate disambiguation | batch_69d88a77e6e88190b7519100bde76575 |
completed | April 10, 2026, 5:28 a.m. |
| PDg | Predicate description generation | batch_69d8938a1f8c81908ffb049fa5fee5a7 |
completed | April 10, 2026, 6:07 a.m. |
Created at: April 8, 2026, 9:40 p.m.