Triple
T11474628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Standarte des Bundeskanzlers |
E271993
|
entity |
| Predicate | usedByOffice |
P8536
|
FINISHED |
| Object |
Bundeskanzlerin
Die Bundeskanzlerin ist die Regierungschefin der Bundesrepublik Deutschland und leitet die Bundesregierung.
|
E53505
|
NE FINISHED |
How this triple was built (4 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: Bundeskanzlerin | Statement: [Standarte des Bundeskanzlers, usedByOffice, Bundeskanzlerin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bundeskanzlerin Context triple: [Standarte des Bundeskanzlers, usedByOffice, Bundeskanzlerin]
-
A.
Frau Bundeskanzlerin
Frau Bundeskanzlerin is the formal German mode of address used for a woman serving as the Federal Chancellor of Germany.
-
B.
Herr Bundeskanzler
Herr Bundeskanzler is the formal German honorific style used to address the Federal Chancellor of Austria.
-
C.
Max Merkel
Max Merkel was a prominent Austrian football manager known for leading several European clubs to success in the 1960s and 1970s.
-
D.
Una Merkel
Una Merkel was an American stage and film actress best known for her sharp comic timing and memorable supporting roles in Hollywood films of the 1930s and 1940s.
-
E.
Bettina Wulff
Bettina Wulff is a German public relations consultant and former First Lady of Germany, known for her marriage to former President Christian Wulff and her subsequent media presence.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bundeskanzlerin Triple: [Standarte des Bundeskanzlers, usedByOffice, Bundeskanzlerin]
Generated description
Die Bundeskanzlerin ist die Regierungschefin der Bundesrepublik Deutschland und leitet die Bundesregierung.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bundeskanzlerin Target entity description: Die Bundeskanzlerin ist die Regierungschefin der Bundesrepublik Deutschland und leitet die Bundesregierung.
-
A.
Frau Bundeskanzlerin
chosen
Frau Bundeskanzlerin is the formal German mode of address used for a woman serving as the Federal Chancellor of Germany.
-
B.
Herr Bundeskanzler
Herr Bundeskanzler is the formal German honorific style used to address the Federal Chancellor of Austria.
-
C.
Max Merkel
Max Merkel was a prominent Austrian football manager known for leading several European clubs to success in the 1960s and 1970s.
-
D.
Una Merkel
Una Merkel was an American stage and film actress best known for her sharp comic timing and memorable supporting roles in Hollywood films of the 1930s and 1940s.
-
E.
Bettina Wulff
Bettina Wulff is a German public relations consultant and former First Lady of Germany, known for her marriage to former President Christian Wulff and her subsequent media presence.
- F. None of above.
Provenance (5 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_69d6aae0c8d881908a5a360c0be3242e |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8294c8dc48190a515f83c99405a3b |
completed | April 9, 2026, 10:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e6042507c4819096afc2839fda186d |
completed | April 20, 2026, 10:47 a.m. |
| NEDg | Description generation | batch_69e610a07bf881908de79850edb9576f |
completed | April 20, 2026, 11:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e617fdaaa88190a1860fb00309596b |
completed | April 20, 2026, 12:11 p.m. |
Created at: April 8, 2026, 9:36 p.m.