Triple
T90317
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles Curtis |
E1814
|
entity |
| Predicate | servedInOfficeFrom |
P1814
|
FINISHED |
| Object | 1929 |
—
|
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: 1929 | Statement: [Charles Curtis, servedInOfficeFrom, 1929]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servedInOfficeFrom Context triple: [Charles Curtis, servedInOfficeFrom, 1929]
-
A.
servedAs
Indicates that one entity held and performed the role, position, or function associated with another entity for some period of time.
-
B.
officeHolderStartTime
chosen
Indicates the date and time at which an individual begins holding a particular office or position.
-
C.
firstInOfficeTo
Indicates that one entity was the earliest or first to hold a particular office or position in relation to another entity or context.
-
D.
servedAsPresidentDuring
Indicates that a person held the office of president for the duration of a specified time period or event.
-
E.
electedOffice
Indicates that an entity holds or has held a particular office or position as a result of an election.
- 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_69a24d1a97dc819094e6c021fe9b05a7 |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24feef1b08190bb9525f71cce053e |
completed | Feb. 28, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69a24eb82d408190b0f9c786152e8e4c |
completed | Feb. 28, 2026, 2:11 a.m. |
Created at: Feb. 28, 2026, 2:07 a.m.