Triple
T2544386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | First Mayor of Hamburg |
E57862
|
entity |
| Predicate | officeTypeComparableTo |
P15578
|
FINISHED |
| Object | Minister-President of a German state |
—
|
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: Minister-President of a German state | Statement: [First Mayor of Hamburg, officeTypeComparableTo, Minister-President of a German state]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeTypeComparableTo Context triple: [First Mayor of Hamburg, officeTypeComparableTo, Minister-President of a German state]
-
A.
hasOfficeType
Indicates that an entity’s office is classified as a specific type or category of office.
-
B.
equivalentOffice
chosen
Indicates that two offices are considered functionally or formally the same position, role, or authority, even if they differ in name or jurisdiction.
-
C.
employmentType
Indicates the specific kind or category of employment relationship that exists between an individual and an employer (e.g., full-time, part-time, contract).
-
D.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
E.
officeTypeChange
Indicates a change in the designated type or classification of an office from one category to another.
- 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_69ab4a5212d88190b989ce129f2ad87f |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd2c10ce88190b242ab3d41878fda |
completed | March 7, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69abd0c63964819092d5f578195ae8dd |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:47 p.m.