Triple
T2425017
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frau Bundeskanzlerin |
E53505
|
entity |
| Predicate | refersToOffice |
P14928
|
FINISHED |
| Object | Federal Chancellor of Germany |
—
|
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: Federal Chancellor of Germany | Statement: [Frau Bundeskanzlerin, refersToOffice, Federal Chancellor of Germany]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: refersToOffice Context triple: [Frau Bundeskanzlerin, refersToOffice, Federal Chancellor of Germany]
-
A.
relatesToOffice
chosen
Indicates that one entity has a connection, association, or relevance to an office, its functions, or its environment.
-
B.
includedOffice
Indicates that one office is contained within, or forms part of, another office or organizational unit.
-
C.
equivalentOffice
Indicates that two offices are considered functionally or formally the same position, role, or authority, even if they differ in name or jurisdiction.
-
D.
usedByOffice
Indicates that something is utilized, operated, or employed by an office or office-related entity.
-
E.
worksWithOffice
Indicates that an entity collaborates or is professionally associated with a particular office or office-based organization.
- 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_69ab495c44d48190b7235b23719bc3f6 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abc9f342e88190a430b02842ded418 |
completed | March 7, 2026, 6:47 a.m. |
| PD | Predicate disambiguation | batch_69abc5a889948190b77de4ef6ac815a8 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 6, 2026, 9:42 p.m.