Triple
T11560389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emanuel Parzen |
E274125
|
entity |
| Predicate | hasSurname |
P18
|
FINISHED |
| Object | Parzen |
E274125
|
NE 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: Parzen | Statement: [Emanuel Parzen, hasSurname, Parzen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Parzen Context triple: [Emanuel Parzen, hasSurname, Parzen]
-
A.
Parzen
chosen
Parzen is a surname most notably associated with Emanuel Parzen, an American statistician known for the Parzen window method in probability and statistics.
-
B.
Parnes
Parnes is a mountain in Greece traditionally associated with the ancient Greek personifications of mountains known as the Ourea.
-
C.
Malvar
Malvar is a Filipino surname most notably associated with General Miguel Malvar, a key revolutionary leader during the Philippine–American War.
-
D.
Geiringer
Geiringer is a surname most notably associated with Hilda Geiringer, an Austrian-American mathematician known for her contributions to applied mathematics and probability theory.
-
E.
Pinzberg
Pinzberg is a small municipality in the Upper Franconian region of Bavaria, Germany.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6aae4dfa48190a3ab0b19a159a3c5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d88a899d4481909a3bce3147763b51 |
completed | April 10, 2026, 5:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e6e88b84d48190948243646bb5fd2b |
completed | April 21, 2026, 3:01 a.m. |
Created at: April 8, 2026, 9:37 p.m.