Triple
T23925417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. politicians |
E602337
|
entity |
| Predicate | typicalOffice |
P154387
|
FINISHED |
| Object | President of the United States |
—
|
NE NERFINISHED |
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: President of the United States | Statement: [U.S. politicians, typicalOffice, President of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalOffice Context triple: [U.S. politicians, typicalOffice, President of the United States]
-
A.
officeIn
Indicates that one entity has an office located within the premises or jurisdiction of another entity.
-
B.
officeIs
Indicates that one entity serves as the office or official workplace location of another entity.
-
C.
officeFounded
Indicates that an office or branch of an organization was established or created at a particular time or place.
-
D.
officeUnder
Indicates that one office is subordinate to, managed by, or organizationally within the authority of another office.
-
E.
traditionalOffice
Indicates that the relationship or action occurs within, or is associated with, a conventional physical office environment rather than a remote or nonstandard workspace.
- 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_69e2953b928c819095395fa87baca583 |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f1cf1ce7f88190a4afd091b4384558 |
completed | April 29, 2026, 9:27 a.m. |
| PD | Predicate disambiguation | batch_69f16151ebdc819086e9e1d7cc1f4f3c |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f16e348b548190b76e50f9b611f76d |
completed | April 29, 2026, 2:34 a.m. |
Created at: April 17, 2026, 8:44 p.m.