Triple
T14151236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andreas Pilger |
E350683
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Andreas Pilger |
—
|
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: Andreas Pilger | Statement: [Andreas Pilger, name, Andreas Pilger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Andreas Pilger Context triple: [Andreas Pilger, name, Andreas Pilger]
-
A.
Andreas Pilger
chosen
Andreas Pilger is a person notable enough to be recognized as a bearer of the surname Pilger, though specific widely known public details about him are not clearly established.
-
B.
Wolfgang Pilger
Wolfgang Pilger is a notable individual recognized as a bearer of the surname Pilger.
-
C.
Andreas Senger
Andreas Senger is a person bearing the surname Senger, about whom no widely documented public information is available.
-
D.
Joachim Haspinger
Joachim Haspinger was a Capuchin priest and military leader who became a prominent figure in the Tyrolean uprising against Napoleonic and Bavarian rule in 1809.
-
E.
Peter Zimroth
Peter Zimroth was an American lawyer and legal scholar who served as New York City’s Corporation Counsel and later as the court-appointed monitor overseeing reforms to the NYPD’s stop-and-frisk practices.
- 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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6124e23481909e5132a40a1d8624 |
completed | April 14, 2026, 3:45 p.m. |
Created at: April 10, 2026, 12:57 a.m.