Triple
T11725377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morris L. Eaton |
E278750
|
entity |
| Predicate | educatedBy |
P335
|
FINISHED |
| Object | Emanuel Parzen |
E55391
|
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: Emanuel Parzen | Statement: [Morris L. Eaton, educatedBy, Emanuel Parzen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emanuel Parzen Context triple: [Morris L. Eaton, educatedBy, Emanuel Parzen]
-
A.
Emanuel Parzen
chosen
Emanuel Parzen was an American statistician renowned for pioneering kernel density estimation, particularly through the development of the Parzen window method.
-
B.
Herbert Parzen
Herbert Parzen was a Jewish scholar and historian known for his contributions to the study of Jewish history and culture.
-
C.
Dennis Michie
Dennis Michie was a U.S. Army officer and early football coach at West Point who is honored as the namesake of the United States Military Academy’s Michie Stadium.
-
D.
Solomon Kullback
Solomon Kullback was an American statistician and cryptanalyst best known for co-developing the Kullback–Leibler divergence, a fundamental concept in information theory and statistics.
-
E.
Zvi Kohavi
Zvi Kohavi was a computer scientist and academic known for his influential work and textbooks in automata theory, switching and finite automata, and the mathematical foundations of computation.
- 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_69d6aaffec6881908bead509e8621742 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4d603cc8190b2e68d0bdd793362 |
completed | April 10, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f1306859208190bc5d0b4f81fb1bff |
completed | April 28, 2026, 10:10 p.m. |
Created at: April 8, 2026, 9:41 p.m.