Triple
T32280412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madam President |
E824672
|
entity |
| Predicate | officeTypeContext |
P14964
|
FINISHED |
| Object | magical government executive |
—
|
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: magical government executive | Statement: [Madam President, officeTypeContext, magical government executive]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeTypeContext Context triple: [Madam President, officeTypeContext, magical government executive]
-
A.
officeTermContext
Indicates the contextual circumstances, such as time period or conditions, under which a particular office term or tenure is held or applies.
-
B.
officeIs
Indicates that one entity serves as the office or official workplace location of another entity.
-
C.
officeCategory
chosen
Indicates the classification or type of an office within a defined categorization scheme.
-
D.
officeTypeChange
Indicates a change in the designated type or classification of an office from one category to another.
-
E.
officeIn
Indicates that one entity has an office located within the premises or jurisdiction of another entity.
- 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_69f3490f404081908450db66884f4334 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69ff17be6ad48190963206f2619b1b28 |
completed | May 9, 2026, 11:17 a.m. |
| PD | Predicate disambiguation | batch_69ff1724ba24819092c928fcbcb286ec |
completed | May 9, 2026, 11:14 a.m. |
Created at: May 1, 2026, 12:43 a.m.