Triple
T8494916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Silverberg |
E201073
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Silberberg |
E201073
|
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: Silberberg | Statement: [Silverberg, hasVariant, Silberberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Silberberg Context triple: [Silverberg, hasVariant, Silberberg]
-
A.
Silverberg
chosen
Silverberg is a surname most notably associated with American science fiction author Robert Silverberg.
-
B.
Schatzberg
Schatzberg is a surname most notably associated with Jerry Schatzberg, an American photographer and film director known for works like "Panic in Needle Park" and "Scarecrow."
-
C.
Blaustein
Blaustein is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, situated near the city of Ulm.
-
D.
Eisenberg
Eisenberg is a surname most notably associated with American actress Hallie Kate Eisenberg.
-
E.
Eisenberg
Eisenberg is a small town in the German state of Thuringia that serves as an administrative and cultural center for its surrounding rural district.
- 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_69ca831ee390819095fae73400bbfafc |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe57c01f881908cb77c8c834ac08d |
completed | March 31, 2026, 3:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce3a6117988190b8c667d4263f3e86 |
completed | April 2, 2026, 9:44 a.m. |
Created at: March 30, 2026, 6:13 p.m.