Triple
T4426841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leopold Zunz |
E95229
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Leopold |
E110183
|
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: Leopold | Statement: [Leopold Zunz, givenName, Leopold]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leopold Context triple: [Leopold Zunz, givenName, Leopold]
-
A.
Leopold
chosen
Leopold is a masculine given name of Germanic origin historically borne by various European rulers, saints, and notable figures.
-
B.
Ludwik
Ludwik is a given name, primarily used in Polish, that corresponds to the name Ludwig in other European languages.
-
C.
Ernst
Ernst is a masculine given name of Germanic origin, commonly used in German-speaking and Scandinavian countries.
-
D.
Günther
Günther is a German masculine given name traditionally associated with figures of Germanic origin and culture.
-
E.
Theodor
Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
- 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_69b3453c2a0c8190926b574c90766db9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3554fb28081909018eaecc0c5c230 |
completed | March 13, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5f633a69c8190b062c2a78b0f8319 |
completed | March 14, 2026, 11:58 p.m. |
Created at: March 12, 2026, 11:30 p.m.