Triple
T11674936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Presidential Office Building, Taipei |
E277466
|
entity |
| Predicate | becamePresidentialOffice |
P100719
|
FINISHED |
| Object | 1950 |
—
|
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: 1950 | Statement: [Presidential Office Building, Taipei, becamePresidentialOffice, 1950]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: becamePresidentialOffice Context triple: [Presidential Office Building, Taipei, becamePresidentialOffice, 1950]
-
A.
succeededToOfficeIn
Indicates that one entity assumed or took over an office or position previously held by another entity at a specified time or in a specified succession.
-
B.
officeHolderBecame
Indicates that an individual assumed or transitioned into holding a particular office or official position.
-
C.
succeededByAsPresident
Indicates that one individual directly followed another in holding the office of president.
-
D.
wonPresidencyWith
Indicates that one entity attained the presidency by means of, or through the support, strategy, or circumstances provided by, another entity.
-
E.
hadMajorPresidency
Indicates that the subject held a primary or significant presidential office over the object entity (such as a country, organization, or institution).
- 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.