Triple
T20556470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Max Merkel |
E504732
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Merkel |
—
|
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: Merkel | Statement: [Max Merkel, familyName, Merkel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Merkel Context triple: [Max Merkel, familyName, Merkel]
-
A.
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.
-
B.
Max Merkel
chosen
Max Merkel was a prominent Austrian football manager known for leading several European clubs to success in the 1960s and 1970s.
-
C.
Angela Merkel
Angela Merkel is a German politician who served as Chancellor of Germany from 2005 to 2021 and became one of the most influential leaders in Europe and the world.
-
D.
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.
-
E.
Merklín
Merklín is a small municipality and village located in the Plzeň Region of the Czech Republic.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4b6587c8190aee63dc7cff244ea |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a5de9c008190b8620628fb285e90 |
completed | April 20, 2026, 10:17 p.m. |
Created at: April 16, 2026, 11:38 a.m.