Triple
T18093588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Black Rider |
E433029
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Wilhelm |
—
|
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: Wilhelm | Statement: [The Black Rider, featuresCharacter, Wilhelm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wilhelm Context triple: [The Black Rider, featuresCharacter, Wilhelm]
-
A.
Wilhelm
chosen
Wilhelm is a Germanic given name, equivalent to William, historically borne by numerous European nobles, rulers, and notable figures.
-
B.
Gerhard
Gerhard is a masculine given name of German origin, historically common in German-speaking countries.
-
C.
Gustav
Gustav is a masculine given name of German origin, borne by several notable historical figures including scientists, artists, and royalty.
-
D.
Hermann
Hermann is a German surname borne by various notable individuals across fields such as philosophy, science, and the arts.
-
E.
Hermann
Hermann is a French publishing house best known for issuing influential mathematical works, including the foundational texts of the Bourbaki school.
- 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_69d8b907d05c819083cc3bd6021089e6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4dd1a75048190924ebc01da83851b |
completed | April 19, 2026, 1:48 p.m. |
Created at: April 10, 2026, 10:27 a.m.