Triple
T32280400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madam President |
E824672
|
entity |
| Predicate | abbreviationOfOfficeHolderTitle |
P104442
|
FINISHED |
| Object | President of MACUSA |
—
|
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 MACUSA | Statement: [Madam President, abbreviationOfOfficeHolderTitle, President of MACUSA]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: abbreviationOfOfficeHolderTitle Context triple: [Madam President, abbreviationOfOfficeHolderTitle, President of MACUSA]
-
A.
academicTitleAbbreviation
Indicates that one entity is the standard abbreviated form of another entity’s full academic title.
-
B.
typicalOfficeHolderTitle
Indicates the standard or commonly used title typically held by the office holder of a given position or role.
-
C.
officeHolderTitle
Indicates the official position or title held by a person in an office or role.
-
D.
professionalTitleAbbreviation
Indicates that one entity is an abbreviated form of a professional title associated with another entity.
-
E.
officeHoldersAreAddressedAs
chosen
Indicates that individuals holding a particular office or position are referred to by a specific title or form of address.
- 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_69f6fb19063c81909466b329655c8583 |
completed | May 3, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f6f96badb08190994442c2aba840b1 |
completed | May 3, 2026, 7:29 a.m. |
Created at: May 1, 2026, 12:43 a.m.