Triple
T19783337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samuel Pinsker |
E475193
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Pinsker |
—
|
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: Pinsker | Statement: [Samuel Pinsker, familyName, Pinsker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pinsker Context triple: [Samuel Pinsker, familyName, Pinsker]
-
A.
Pinsker
chosen
Pinsker is a Jewish surname most notably associated with Leo Pinsker, a 19th-century physician and early Zionist activist.
-
B.
Pincus
Pincus is a surname most notably associated with Lionel Pincus, an influential American financier and co-founder of the private equity firm Warburg Pincus.
-
C.
Bonger
Bonger is a Dutch surname most notably associated with Johanna van Gogh-Bonger, the key figure in preserving and promoting Vincent van Gogh’s artistic legacy.
-
D.
Pinzberg
Pinzberg is a small municipality in the Upper Franconian region of Bavaria, Germany.
-
E.
Eisele
Eisele is a surname most notably associated with Donn F. Eisele, an American astronaut who flew on the Apollo 7 mission.
- 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65385ee8081908d58cc3ff05b9b23 |
completed | April 20, 2026, 4:25 p.m. |
Created at: April 10, 2026, 1:49 p.m.