Triple
T17616124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Markus Wolf |
E429087
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Markus Wolf |
—
|
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: Markus Wolf | Statement: [Markus Wolf, name, Markus Wolf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Markus Wolf Context triple: [Markus Wolf, name, Markus Wolf]
-
A.
Markus Wolf
chosen
Markus Wolf was a prominent East German spymaster who led the foreign intelligence service of the Stasi and became one of the Cold War’s most influential intelligence chiefs.
-
B.
Markus Osthoff
Markus Osthoff is a retired German footballer who played as a midfielder, best known for his time in the Bundesliga during the 1990s.
-
C.
Markus Häußler
Markus Häußler is a German local politician who serves as the mayor of the town of Munderkingen in Baden-Württemberg.
-
D.
Markus Häußler
Markus Häußler is a German local politician who serves as the mayor of the municipality of Illerkirchberg in Baden-Württemberg.
-
E.
Markus Morgenstern
Markus Morgenstern is a mathematician known for his contributions to combinatorics and graph theory.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46d32991c81909801161b0a416c94 |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 5:51 a.m.